DYNAMIC MALWARE CLASSIFICATION
Supervised, classification, multi-class
0. Basic Set-Up and General info¶
Note: there are a lot of non-used imports (to be removed at the end)
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import seaborn as sns
from scipy import stats
from scipy.stats import loguniform
from skopt.space import Real
import copy
import yaml
In [2]:
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import StackingClassifier
In [3]:
from imblearn.over_sampling import SMOTENC
from boruta import BorutaPy
In [4]:
from library.utils.ownModels.majorityClassModel import MajorityClassClassifier # Importing self-developed model object
from library.utils.ownModels.neuralNets.feedForward import FeedForwardNeuralNetwork
In [5]:
import importlib
# Import your modules initially
import library.pipeline
# Reload the modules in case they were updated
importlib.reload(library.pipeline)
# Now you can import specific classes/functions
from library.pipeline.pipeline import Pipeline
from library.pipeline.pipeline_manager import PipelineManager
Documentation Standarization¶
Follow the following guidelines to make sure our document is consistent and easier to undestand
- All constant should be writte in full capitall letters (e.g: MY_CONSTANT). Notebook-level constants should be written in the cell assigned to it (see below)
- Every new major section (EDA, Feature engineering) should be written with '# My title'#
- Subsequent sections should use '##', '###' or '####' hierarchacically
- At the beginning of each major section write a content index with the content included in that section (see 'feature engineering' section as an example)
- Please write paragraphs before and after each cell explain what u are about to do and the conclusions, correspondingly. Do not assume they are too obvious.
- Avoid excessive ChatGPT-originated comments
- Avoid writing more than 20 lines per code cell (exceptions for subroutines, which should be written in utilitied_functions.py)
- Ideally, add a "Questions" and "Things to be done" section in each major sections where u write about futher iterations u want to do (while sharing in with the rest)
Set-up¶
In [6]:
import yaml
In [7]:
variables = yaml.load(open("library/configurations.yaml"), Loader=yaml.FullLoader)
We start off with a pipeline for all the models. Then at some moment, whenever we think the pipelines will diverge, we remove the by-object-refrence (that we will create in the next cell), and create a new copy with our modifications ready.
Pipeline Selection¶
We will having pipelines for:
- ensembled_models (random forest, gradient boosting)
- tree-based (decision trees)
- support vectors machines (non-linear*)
- naive bayes (gaussian naive bayes)
- stacking (stacking models)
- We originally conceived to add linear for the sake of model diversity. While the theory already pointed us out that the linear SVM wont be able to find the separating hyperplane (i.e., model is able to fit), we wanted to see if by setting
In [8]:
default_pipeline = ensembled_pipeline = tree_pipeline = supportVectorsMachine_pipeline = baseline_pipeline = naiveBayes_pipeline = stacking_pipeline = feedForwardNN_pipeline = example = Pipeline(
dataset_path=variables["PIPELINE_RUNNER"]["dataset_path"],
model_results_path="results/model_evaluation/results.csv",
model_task="classification")
In [9]:
pipelines = {
"not_baseline": {
"ensembled": ensembled_pipeline,
"tree_based": tree_pipeline,
"support_vector_machine": supportVectorsMachine_pipeline,
"naive_bayes": naiveBayes_pipeline,
"feed_forward_neural_network": feedForwardNN_pipeline,
"stacking": stacking_pipeline,
},
"baseline": {
"baselines": baseline_pipeline,
"example": example
}
}
pipeline_manager = PipelineManager(pipelines)
Here I show an example of how we will be working with by-object-reference.
In [10]:
baseline_pipeline.dataset.example_attribute = "1"
In [11]:
default_pipeline.dataset.example_attribute
Out[11]:
'1'
However, if we do a (deep) copy: Note that the difference between a deep and a shallow copy is that the former copies all the objects that are inside the class. We need that. We will overwrite the prior attribute and show is this is not propagated to the other results anymore
In [12]:
import copy
In [13]:
new_pipeline = copy.deepcopy(default_pipeline)
new_pipeline.dataset.example_attribute = "2"
In [14]:
default_pipeline.dataset.example_attribute
Out[14]:
'1'
It may also be of interest for you to propagate the same method's invokation to all the pipelines within the pipeline manager. For instance, after a divergence in, lets say, feature scaling, you may need to fit all the models in the corresponding pipelines. You would want to avoid reduntandly calling the same method with the same parameters for all the Pipeline class instances. In order to do so, we present an example that avoids such annoyance
In [15]:
pipeline_manager.create_pipeline_divergence(category="baseline", pipelineName="example", print_results=True)
Pipeline example in category baseline has diverged
Pipeline schema is now: {'not_baseline': {'ensembled': <library.pipeline.pipeline.Pipeline object at 0x0000018C2C300740>, 'tree_based': <library.pipeline.pipeline.Pipeline object at 0x0000018C2C300740>, 'support_vector_machine': <library.pipeline.pipeline.Pipeline object at 0x0000018C2C300740>, 'naive_bayes': <library.pipeline.pipeline.Pipeline object at 0x0000018C2C300740>, 'feed_forward_neural_network': <library.pipeline.pipeline.Pipeline object at 0x0000018C2C300740>, 'stacking': <library.pipeline.pipeline.Pipeline object at 0x0000018C2C300740>}, 'baseline': {'baselines': <library.pipeline.pipeline.Pipeline object at 0x0000018C2C300740>, 'example': <library.pipeline.pipeline.Pipeline object at 0x0000018C2EB5C200>}}
Out[15]:
<library.pipeline.pipeline.Pipeline at 0x18c2eb5c200>
In [16]:
pipeline_manager.pipelines["baseline"]["example"].feature_analysis.feature_selection.automatic_feature_selection.speak("Whats goooddd")
Whats goooddd from 1701590722672. You are at automatic feature selection!
In [17]:
pipeline_manager.all_pipelines_execute(methodName="speak", message="Hello, world!")
Hello, world! from 1701548394304 Hello, world! from 1701590712832
Out[17]:
{'not_baseline': {'ensembled': None}, 'baseline': {'example': None}}
A method present deeper down the class:
Instead of doing: 'pipeline_manager.pipelines["baseline"]["logistic"].feature_analysis.feature_selection.automatic_feature_selection.speak("Whats goooddd") for all objects'
we can do:
In [18]:
pipeline_manager.all_pipelines_execute(methodName="feature_analysis.feature_selection.automatic_feature_selection.speak", message="Whats good")
Whats good from 1701443978496. You are at automatic feature selection! Whats good from 1701590722672. You are at automatic feature selection!
Out[18]:
{'not_baseline': {'ensembled': None}, 'baseline': {'example': None}}
In [19]:
# lets delete the example obj
del new_pipeline
del pipeline_manager.pipelines["baseline"]["example"]
In [20]:
pipeline_manager.pipelines
Out[20]:
{'not_baseline': {'ensembled': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'tree_based': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'support_vector_machine': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'naive_bayes': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'feed_forward_neural_network': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'stacking': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>},
'baseline': {'baselines': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>}}
Start Of The Pipeline¶
In [21]:
default_pipeline.dataset.df.head()
Out[21]:
| Memory_PssTotal | Memory_PssClean | Memory_SharedDirty | Memory_PrivateDirty | Memory_SharedClean | Memory_PrivateClean | Memory_SwapPssDirty | Memory_HeapSize | Memory_HeapAlloc | Memory_HeapFree | ... | Logcat_error | Logcat_warning | Logcat_debug | Logcat_verbose | Logcat_total | Process_total | Hash | Category | Family | Reboot | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 31053 | 2448 | 14044 | 23472 | 74824 | 2452 | 0 | 8919 | 4786 | 4132 | ... | 1635 | 2351 | 3285 | 1551 | 11221 | 193 | f460abb8f2e4e3fb689966ddaea6d6babbd1738bb691c7... | Trojan_SMS | opfake | before |
| 1 | 107787 | 21976 | 11852 | 74548 | 69052 | 23152 | 0 | 25341 | 20965 | 4375 | ... | 1816 | 826 | 1544 | 2045 | 8457 | 189 | 556c238536d837007e647543eaf3ea95ae9aaf1c1a52d0... | Trojan_SMS | opfake | before |
| 2 | 86584 | 18460 | 12284 | 59992 | 91548 | 19376 | 0 | 24500 | 21378 | 3121 | ... | 2244 | 3406 | 1565 | 2819 | 10780 | 195 | 398322f94b5bfa2a9e7b3756a4cf409764595003280c48... | Trojan_SMS | fakeinst | before |
| 3 | 41248 | 924 | 10328 | 36280 | 55768 | 928 | 0 | 10082 | 7281 | 2800 | ... | 974 | 4134 | 3138 | 1556 | 11739 | 191 | 4a9c14872b2c66165599a969a1a8654bb6887d7a18ab6d... | Trojan_SMS | fakeinst | before |
| 4 | 38621 | 5080 | 12392 | 27388 | 71048 | 5088 | 0 | 9077 | 5750 | 3326 | ... | 936 | 2298 | 3752 | 1992 | 10488 | 188 | 6b37b9b9c170727f706b69731e64da4bbca2638b4237a7... | Trojan_SMS | fakeinst | before |
5 rows × 145 columns
In [22]:
for col in default_pipeline.dataset.df.columns.to_list():
print(col)
Memory_PssTotal Memory_PssClean Memory_SharedDirty Memory_PrivateDirty Memory_SharedClean Memory_PrivateClean Memory_SwapPssDirty Memory_HeapSize Memory_HeapAlloc Memory_HeapFree Memory_Views Memory_ViewRootImpl Memory_AppContexts Memory_Activities Memory_Assets Memory_AssetManagers Memory_LocalBinders Memory_ProxyBinders Memory_ParcelMemory Memory_ParcelCount Memory_DeathRecipients Memory_OpenSSLSockets Memory_WebViews API_Process_android.os.Process_start API_Process_android.app.ActivityManager_killBackgroundProcesses API_Process_android.os.Process_killProcess API_Command_java.lang.Runtime_exec API_Command_java.lang.ProcessBuilder_start API_JavaNativeInterface_java.lang.Runtime_loadLibrary API_JavaNativeInterface_java.lang.Runtime_load API_WebView_android.webkit.WebView_loadUrl API_WebView_android.webkit.WebView_loadData API_WebView_android.webkit.WebView_loadDataWithBaseURL API_WebView_android.webkit.WebView_addJavascriptInterface API_WebView_android.webkit.WebView_evaluateJavascript API_WebView_android.webkit.WebView_postUrl API_WebView_android.webkit.WebView_postWebMessage API_WebView_android.webkit.WebView_savePassword API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled API_FileIO_libcore.io.IoBridge_open API_FileIO_android.content.ContextWrapper_openFileInput API_FileIO_android.content.ContextWrapper_openFileOutput API_FileIO_android.content.ContextWrapper_deleteFile API_Database_android.content.ContextWrapper_openOrCreateDatabase API_Database_android.content.ContextWrapper_databaseList API_Database_android.content.ContextWrapper_deleteDatabase API_Database_android.database.sqlite.SQLiteDatabase_execSQL API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase API_Database_android.database.sqlite.SQLiteDatabase_getPath API_Database_android.database.sqlite.SQLiteDatabase_insert API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict API_Database_android.database.sqlite.SQLiteDatabase_openDatabase API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase API_Database_android.database.sqlite.SQLiteDatabase_query API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory API_Database_android.database.sqlite.SQLiteDatabase_rawQuery API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory API_Database_android.database.sqlite.SQLiteDatabase_update API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict API_Database_android.database.sqlite.SQLiteDatabase_compileStatement API_Database_android.database.sqlite.SQLiteDatabase_create API_IPC_android.content.ContextWrapper_sendBroadcast API_IPC_android.content.ContextWrapper_sendStickyBroadcast API_IPC_android.content.ContextWrapper_startActivity API_IPC_android.content.ContextWrapper_startService API_IPC_android.content.ContextWrapper_stopService API_IPC_android.content.ContextWrapper_registerReceiver API_Binder_android.app.ContextImpl_registerReceiver API_Binder_android.app.ActivityThread_handleReceiver API_Binder_android.app.Activity_startActivity API_Crypto_javax.crypto.spec.SecretKeySpec_$init API_Crypto_javax.crypto.Cipher_doFinal API_Crypto-Hash_java.security.MessageDigest_digest API_Crypto-Hash_java.security.MessageDigest_update API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion API_DeviceInfo_android.os.Debug_isDebuggerConnected API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages API_Network_java.net.URL_openConnection API_Network_org.apache.http.impl.client.AbstractHttpClient_execute API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary API_DexClassLoader_dalvik.system.DexFile_loadDex API_DexClassLoader_dalvik.system.DexFile_loadClass API_DexClassLoader_dalvik.system.DexClassLoader_$init API_Base64_android.util.Base64_decode API_Base64_android.util.Base64_encode API_Base64_android.util.Base64_encodeToString API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting API_SystemManager_android.app.NotificationManager_notify API_SystemManager_android.telephony.TelephonyManager_listen API_SystemManager_android.content.BroadcastReceiver_abortBroadcast API_SMS_android.telephony.SmsManager_sendTextMessage API_SMS_android.telephony.SmsManager_sendMultipartTextMessage API_DeviceData_android.content.ContentResolver_query API_DeviceData_android.content.ContentResolver_registerContentObserver API_DeviceData_android.content.ContentResolver_insert API_DeviceData_android.content.ContentResolver_delete API_DeviceData_android.accounts.AccountManager_getAccountsByType API_DeviceData_android.accounts.AccountManager_getAccounts API_DeviceData_android.location.Location_getLatitude API_DeviceData_android.location.Location_getLongitude API_DeviceData_android.media.AudioRecord_startRecording API_DeviceData_android.media.MediaRecorder_start API_DeviceData_android.os.SystemProperties_get API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages API__sessions Network_TotalReceivedBytes Network_TotalReceivedPackets Network_TotalTransmittedBytes Network_TotalTransmittedPackets Battery_wakelock Battery_service Logcat_info Logcat_error Logcat_warning Logcat_debug Logcat_verbose Logcat_total Process_total Hash Category Family Reboot
In [23]:
default_pipeline.dataset.df["Category"].value_counts()
Out[23]:
Category Riskware 14053 Adware 10980 Trojan 8437 Zero_Day 4475 Ransomware 3411 Trojan_Spy 2840 Trojan_SMS 1939 No_Category 1932 Trojan_Dropper 1570 PUA 1290 Backdoor 1137 Scareware 886 FileInfector 248 Trojan_Banker 241 Name: count, dtype: int64
In [24]:
default_pipeline.dataset.df.rename(columns={"reboot": "Reboot"}, inplace=True) # consistency with other features' names
default_pipeline.dataset.df.drop(columns=["Family", "Hash"], inplace=True) # We have decided to use only category as target variable; Hash is temporary while im debugging (it will be deleted in EDA)
We also have to delete all the records with categories zero day and no category"
In [25]:
default_pipeline.dataset.df.shape[0]
Out[25]:
53439
In [26]:
default_pipeline.dataset.df.drop(default_pipeline.dataset.df[default_pipeline.dataset.df["Category"] == "Zero_Day"].index, inplace=True)
default_pipeline.dataset.df.drop(default_pipeline.dataset.df[default_pipeline.dataset.df["Category"] == "No_Category"].index, inplace=True)
default_pipeline.dataset.df["Category"].value_counts()
Out[26]:
Category Riskware 14053 Adware 10980 Trojan 8437 Ransomware 3411 Trojan_Spy 2840 Trojan_SMS 1939 Trojan_Dropper 1570 PUA 1290 Backdoor 1137 Scareware 886 FileInfector 248 Trojan_Banker 241 Name: count, dtype: int64
In [27]:
default_pipeline.dataset.df.shape[0]
Out[27]:
47032
Description of our features¶
For each of our entries in the dataset we have a feature describing the state of the (operating) system under which the malware was running. Here is an intial description for each of them. Please access this document for a complete problem context description: https://docs.google.com/document/d/1yH9gvnJVSH9GLv9ATQ5JQWA2z8Jy4umxxRfMF-y2fiU/edit?usp=sharing
This section below shall be deleted
In this section, we conduct an Exploratory Data Analysis (EDA) on the dynamic malware dataset to gain initial insights into the structure, distribution, and quality of the data prior to modeling. The dataset includes behavioral features extracted from Android applications, along with labels indicating their respective malware categories. Understanding the composition of the dataset, such as class imbalance, feature correlations, and the presence of outliers, is crucial to ensure robust preprocessing, informed feature engineering, and the success of machine learning classifiers.
SUGGESTIONS FOR IMPROVEMENTS¶
- Cluster analysis
Data Splitting: Category as target variable¶
Originally, we will focus only on category
Lets first get the X and y extracted from our dataset
Also object! Lets get back to the splitting!
Before, we split the dataset lets observe the SE of accuracy variation based on our choice of split. Brief explanation: we can model accuracy via a Binomial distribution. We know each event in a binomial distribution can be modelled through a bernoulli distribution, where the outcome represents the probability predicting the correct class or not. We make the assumption that each classification error is independent from each other. For: $$ \text{Bin} \sim (n, p) $$ let us assume that the parameter of this distribution is p = .85 and n is given by the choice of sample split for the test set. The SE of the sample proportion can be modeled via: $$ \text{SE}_{\hat{p}} = \sqrt{\frac{p(1 - p)}{n}} $$
Before we continue to assess all possible choices of split based on a variant n, also note that that choice of evenly distributed split (e.g: 10% for each hold-out set) between hold-out sets is arbitrary. Proper choice is that which guarantees an equivalent distribution at each hold-out sets, which may not neccesarily be the equivalent split. This brings a new choice of tradeoff between certainity of prediction accuracy (higher test size, smaller validaiton set) but possibly less space for proper hyperparemter optimization or the inverse (higher validation size, smaller set set). As with other tradeoffs, the priority for a given option is rooted in the model's application (which may come derived from a client/employer). For our case, we dont favor either option of the tradeoof, thus we will keep the even hold-out distribution.
Finally, also note that that choice of evenly distributed split between hold-out sets is arbitrary. Proper choice is that which guarantees an equivalent distribution at each hold-out sets, which may not neccesarily be the equivalent split. This brings a new choice of tradeoff between certainity of prediction accuracy (higher test size, smaller validaiton set) but possibly less space for proper hyperparemter optimization or the inverse (higher validation size, smaller set set). As with other tradeoffs, the priority for a given option is rooted in the model's application (which may come derived from a client/employer). For our case, we dont favor either option of the tradeoof, thus we will keep the even hold-out distribution.
In [28]:
default_pipeline.dataset.split.asses_split_classifier(p=.85,
step=.05,
save_plots=False,
save_path=None)
c:\Users\irina\ml-final-project\library\phases\phases_implementation\dataset\split\strategies\noTimeSeries.py:120: RuntimeWarning: divide by zero encountered in scalar divide differenceToPriorSE_percentage = (currentSE - priorSE) / priorSE
Out[28]:
| train_size (%) | train_size_count | validation_size (%) | validation_size_count | test_size (%) | test_size_coount | currentSE | differenceToPriorSE | differenceToPriorSE (%) | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.95 | 44680.0 | 0.025 | 1176.0 | 0.025 | 1176.0 | 0.010412 | 0.010412 | inf |
| 1 | 0.90 | 42329.0 | 0.050 | 2352.0 | 0.050 | 2352.0 | 0.007363 | -0.003050 | -0.292893 |
| 2 | 0.85 | 39977.0 | 0.075 | 3527.0 | 0.075 | 3527.0 | 0.006012 | -0.001350 | -0.183388 |
| 3 | 0.80 | 37626.0 | 0.100 | 4703.0 | 0.100 | 4703.0 | 0.005207 | -0.000806 | -0.134005 |
| 4 | 0.75 | 35274.0 | 0.125 | 5879.0 | 0.125 | 5879.0 | 0.004657 | -0.000550 | -0.105592 |
| 5 | 0.70 | 32922.0 | 0.150 | 7055.0 | 0.150 | 7055.0 | 0.004251 | -0.000406 | -0.087142 |
| 6 | 0.65 | 30571.0 | 0.175 | 8231.0 | 0.175 | 8231.0 | 0.003936 | -0.000315 | -0.074189 |
| 7 | 0.60 | 28219.0 | 0.200 | 9406.0 | 0.200 | 9406.0 | 0.003682 | -0.000254 | -0.064543 |
| 8 | 0.55 | 25868.0 | 0.225 | 10582.0 | 0.225 | 10582.0 | 0.003471 | -0.000211 | -0.057202 |
| 9 | 0.50 | 23516.0 | 0.250 | 11758.0 | 0.250 | 11758.0 | 0.003293 | -0.000178 | -0.051326 |
We can see a diminishing-returns class graph. The more we decline the training set percentage the slower and more steadier the current SE varies as well as the difference to prior SE, in percentage. We can see that the knee in the curve is between 80 and 90 training set percentage. This represents the area where when you start going below the lower bound, no significant improve appears. Given the fact that our criteria for choice of split percentage is to keep as much training possible while increasing hold-out sets size only if the decrease in SE is significant we are going to select 80% for the training set.
In [29]:
default_pipeline.dataset.split.split_data(y_column="Category",
train_size=.8,
validation_size=.1,
test_size=.1,
save_plots=False,
save_path=None)
X_train: (37625, 142) X_val: (4703, 142) X_test: (4704, 142) y_train: (37625,) y_val: (4703,) y_test: (4704,)
Out[29]:
{'Memory_PssTotal': {6520: 43041,
2425: 125178,
44969: 43428,
50354: 34651,
21242: 80981},
'Memory_PssClean': {6520: 6544,
2425: 31456,
44969: 2288,
50354: 804,
21242: 32356},
'Memory_SharedDirty': {6520: 12304,
2425: 12704,
44969: 10748,
50354: 11008,
21242: 10760},
'Memory_PrivateDirty': {6520: 28616,
2425: 84372,
44969: 32624,
50354: 29224,
21242: 37032},
'Memory_SharedClean': {6520: 93928,
2425: 85144,
44969: 84988,
50354: 73772,
21242: 92240},
'Memory_PrivateClean': {6520: 6556,
2425: 32908,
44969: 4792,
50354: 812,
21242: 33756},
'Memory_SwapPssDirty': {6520: 0, 2425: 0, 44969: 0, 50354: 0, 21242: 0},
'Memory_HeapSize': {6520: 14237,
2425: 36882,
44969: 19668,
50354: 8070,
21242: 10749},
'Memory_HeapAlloc': {6520: 12368,
2425: 29212,
44969: 16381,
50354: 6645,
21242: 9088},
'Memory_HeapFree': {6520: 1868,
2425: 7669,
44969: 3286,
50354: 1424,
21242: 1660},
'Memory_Views': {6520: 7, 2425: 62, 44969: 44, 50354: 0, 21242: 12},
'Memory_ViewRootImpl': {6520: 1, 2425: 1, 44969: 3, 50354: 0, 21242: 0},
'Memory_AppContexts': {6520: 5, 2425: 4, 44969: 5, 50354: 4, 21242: 4},
'Memory_Activities': {6520: 1, 2425: 1, 44969: 2, 50354: 0, 21242: 1},
'Memory_Assets': {6520: 2, 2425: 4, 44969: 2, 50354: 2, 21242: 4},
'Memory_AssetManagers': {6520: 0, 2425: 0, 44969: 0, 50354: 0, 21242: 0},
'Memory_LocalBinders': {6520: 16, 2425: 37, 44969: 11, 50354: 4, 21242: 14},
'Memory_ProxyBinders': {6520: 29, 2425: 36, 44969: 29, 50354: 24, 21242: 29},
'Memory_ParcelMemory': {6520: 8, 2425: 9, 44969: 6, 50354: 4, 21242: 11},
'Memory_ParcelCount': {6520: 19, 2425: 36, 44969: 24, 50354: 16, 21242: 31},
'Memory_DeathRecipients': {6520: 0, 2425: 3, 44969: 0, 50354: 0, 21242: 0},
'Memory_OpenSSLSockets': {6520: 0, 2425: 0, 44969: 0, 50354: 0, 21242: 0},
'Memory_WebViews': {6520: 0, 2425: 3, 44969: 0, 50354: 0, 21242: 1},
'API_Process_android.os.Process_start': {6520: 0,
2425: 0,
44969: 0,
50354: 0,
21242: 0},
'API_Process_android.app.ActivityManager_killBackgroundProcesses': {6520: 0,
2425: 0,
44969: 0,
50354: 0,
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'Process_total': {6520: 186, 2425: 190, 44969: 187, 50354: 188, 21242: 183},
'Reboot': {6520: 'before',
2425: 'before',
44969: 'before',
50354: 'before',
21242: 'before'}}
In [30]:
default_pipeline.dataset.X_train.shape, default_pipeline.dataset.X_val.shape, default_pipeline.dataset.X_test.shape, default_pipeline.dataset.y_train.shape, default_pipeline.dataset.y_val.shape, default_pipeline.dataset.y_test.shape
Out[30]:
((37625, 142), (4703, 142), (4704, 142), (37625,), (4703,), (4704,))
4. FEATURE ANALYSIS¶
Here we will take adjust the features that compose the learning inputs to our model. The correctness of this section is pivotal for proper learning by the model
FEATURE ENGINEERING¶
- Domain-specific features
- Binning
- Interaction terms
In [31]:
featuresToEncode = ["Reboot"]
In [32]:
encoded_maps_perPipeline = pipeline_manager.all_pipelines_execute(methodName="feature_analysis.feature_transformation.get_categorical_features_encoded", verbose=True, features=featuresToEncode, encode_y=True)
encoded_maps_perPipeline
Pipeline ensembled in category not_baseline has executed feature_analysis.feature_transformation.get_categorical_features_encoded. Result is: {'Adware': 0, 'Backdoor': 1, 'FileInfector': 2, 'PUA': 3, 'Ransomware': 4, 'Riskware': 5, 'Scareware': 6, 'Trojan': 7, 'Trojan_Banker': 8, 'Trojan_Dropper': 9, 'Trojan_SMS': 10, 'Trojan_Spy': 11}
Out[32]:
{'not_baseline': {'ensembled': {'Adware': 0,
'Backdoor': 1,
'FileInfector': 2,
'PUA': 3,
'Ransomware': 4,
'Riskware': 5,
'Scareware': 6,
'Trojan': 7,
'Trojan_Banker': 8,
'Trojan_Dropper': 9,
'Trojan_SMS': 10,
'Trojan_Spy': 11}},
'baseline': {}}
In [33]:
pipeline_manager.pipelines_analysis.encoded_map = encoded_maps_perPipeline["not_baseline"]["ensembled"] # setting baseline as an arbitrary example (all are the same as for now)
Lets visualize the results of the encoding...
In [34]:
default_pipeline.dataset.X_train
Out[34]:
| Memory_PssTotal | Memory_PssClean | Memory_SharedDirty | Memory_PrivateDirty | Memory_SharedClean | Memory_PrivateClean | Memory_SwapPssDirty | Memory_HeapSize | Memory_HeapAlloc | Memory_HeapFree | ... | Battery_wakelock | Battery_service | Logcat_info | Logcat_error | Logcat_warning | Logcat_debug | Logcat_verbose | Logcat_total | Process_total | Reboot_before | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6520 | 43041 | 6544 | 12304 | 28616 | 93928 | 6556 | 0 | 14237 | 12368 | 1868 | ... | 3 | 2 | 3024 | 4730 | 1510 | 1127 | 4815 | 15206 | 186 | 1 |
| 2425 | 125178 | 31456 | 12704 | 84372 | 85144 | 32908 | 0 | 36882 | 29212 | 7669 | ... | 3 | 0 | 1724 | 2295 | 752 | 11157 | 1514 | 17442 | 190 | 1 |
| 44969 | 43428 | 2288 | 10748 | 32624 | 84988 | 4792 | 0 | 19668 | 16381 | 3286 | ... | 3 | 0 | 1376 | 649 | 1478 | 2121 | 1709 | 7333 | 187 | 1 |
| 50354 | 34651 | 804 | 11008 | 29224 | 73772 | 812 | 0 | 8070 | 6645 | 1424 | ... | 4 | 1 | 1468 | 466 | 4828 | 1355 | 1511 | 9628 | 188 | 1 |
| 21242 | 80981 | 32356 | 10760 | 37032 | 92240 | 33756 | 0 | 10749 | 9088 | 1660 | ... | 4 | 1 | 1271 | 4007 | 3247 | 1885 | 4913 | 15323 | 183 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 49104 | 80448 | 2476 | 10352 | 70328 | 58332 | 4364 | 0 | 36495 | 29908 | 6586 | ... | 4 | 1 | 1551 | 1870 | 783 | 2859 | 2296 | 9359 | 189 | 1 |
| 42415 | 44710 | 5304 | 11572 | 30416 | 67004 | 5320 | 0 | 7949 | 5545 | 2403 | ... | 2 | 0 | 3249 | 1181 | 7295 | 1630 | 2900 | 16255 | 187 | 1 |
| 52672 | 89585 | 1072 | 10880 | 82664 | 84236 | 1084 | 0 | 26521 | 22153 | 4367 | ... | 2 | 0 | 1956 | 982 | 2593 | 2741 | 131 | 8403 | 193 | 0 |
| 24471 | 45050 | 7588 | 12352 | 28748 | 86512 | 7612 | 0 | 13236 | 10377 | 2858 | ... | 6 | 2 | 1550 | 1841 | 743 | 6582 | 2485 | 13201 | 186 | 1 |
| 35720 | 62856 | 3936 | 10692 | 46164 | 95492 | 10148 | 0 | 25490 | 19053 | 6436 | ... | 3 | 0 | 119 | 2048 | 2405 | 629 | 9633 | 14834 | 192 | 0 |
37625 rows × 142 columns
In [35]:
default_pipeline.dataset.y_train
Out[35]:
6520 0
2425 0
44969 7
50354 1
21242 5
..
49104 4
42415 11
52672 3
24471 5
35720 5
Length: 37625, dtype: int64
In [36]:
baseline_pipeline.dataset.X_train
Out[36]:
| Memory_PssTotal | Memory_PssClean | Memory_SharedDirty | Memory_PrivateDirty | Memory_SharedClean | Memory_PrivateClean | Memory_SwapPssDirty | Memory_HeapSize | Memory_HeapAlloc | Memory_HeapFree | ... | Battery_wakelock | Battery_service | Logcat_info | Logcat_error | Logcat_warning | Logcat_debug | Logcat_verbose | Logcat_total | Process_total | Reboot_before | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6520 | 43041 | 6544 | 12304 | 28616 | 93928 | 6556 | 0 | 14237 | 12368 | 1868 | ... | 3 | 2 | 3024 | 4730 | 1510 | 1127 | 4815 | 15206 | 186 | 1 |
| 2425 | 125178 | 31456 | 12704 | 84372 | 85144 | 32908 | 0 | 36882 | 29212 | 7669 | ... | 3 | 0 | 1724 | 2295 | 752 | 11157 | 1514 | 17442 | 190 | 1 |
| 44969 | 43428 | 2288 | 10748 | 32624 | 84988 | 4792 | 0 | 19668 | 16381 | 3286 | ... | 3 | 0 | 1376 | 649 | 1478 | 2121 | 1709 | 7333 | 187 | 1 |
| 50354 | 34651 | 804 | 11008 | 29224 | 73772 | 812 | 0 | 8070 | 6645 | 1424 | ... | 4 | 1 | 1468 | 466 | 4828 | 1355 | 1511 | 9628 | 188 | 1 |
| 21242 | 80981 | 32356 | 10760 | 37032 | 92240 | 33756 | 0 | 10749 | 9088 | 1660 | ... | 4 | 1 | 1271 | 4007 | 3247 | 1885 | 4913 | 15323 | 183 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 49104 | 80448 | 2476 | 10352 | 70328 | 58332 | 4364 | 0 | 36495 | 29908 | 6586 | ... | 4 | 1 | 1551 | 1870 | 783 | 2859 | 2296 | 9359 | 189 | 1 |
| 42415 | 44710 | 5304 | 11572 | 30416 | 67004 | 5320 | 0 | 7949 | 5545 | 2403 | ... | 2 | 0 | 3249 | 1181 | 7295 | 1630 | 2900 | 16255 | 187 | 1 |
| 52672 | 89585 | 1072 | 10880 | 82664 | 84236 | 1084 | 0 | 26521 | 22153 | 4367 | ... | 2 | 0 | 1956 | 982 | 2593 | 2741 | 131 | 8403 | 193 | 0 |
| 24471 | 45050 | 7588 | 12352 | 28748 | 86512 | 7612 | 0 | 13236 | 10377 | 2858 | ... | 6 | 2 | 1550 | 1841 | 743 | 6582 | 2485 | 13201 | 186 | 1 |
| 35720 | 62856 | 3936 | 10692 | 46164 | 95492 | 10148 | 0 | 25490 | 19053 | 6436 | ... | 3 | 0 | 119 | 2048 | 2405 | 629 | 9633 | 14834 | 192 | 0 |
37625 rows × 142 columns
In [37]:
baseline_pipeline.dataset.y_train
Out[37]:
6520 0
2425 0
44969 7
50354 1
21242 5
..
49104 4
42415 11
52672 3
24471 5
35720 5
Length: 37625, dtype: int64
FEATURE SELECTION¶
- Analyze correlation and low-variances
Feature selection¶
As explained before, we now proceed to carefully reduce the present high-dimensionality. High-dimensionality increases the chances of the model overfitting (capturing noise from irrelevant features; increasing variance and reducing bias), as well as introducing a significant computational overhead. We match this high-dimensionality with a highly filtering models_pipeline of feature selection. The most extensive cut comes given at the first level, with the mutual information threshold-based cut. This metric captures the level of uncertainity between the feature and the target variable (cnt). In marked contrast with pearson coefficeint (correlation), it is able to model non-linear and linea relationships altoghther. This feature-selectin models_pipeline is compromised of five (3 as of the final models_pipeline) cuts:
- mutual information
- low variance
- multicolinearity analysis
- PCA
- Boruta and/or Lasso
The different thresholds for each of this cuts have been altered over the different models_pipeline iterations. Specifically, in the bias-variance tradeoff (to be elaborated in further detail later), I increased all the thresholds in order to avoid por performance.
Feature to target variable mutual information¶
In [37]:
# models_pipeline.feature_analysis.feature_selection.manual_feature_selection.fit(type="MutualInformation", threshold=.2, delete_features=True, plot=True)
Eliminating low-variances features¶
Features with low variances provide little new information for the model to learn from, thus they could introduce statistical noise. Due to this reason, they should be elimanted from the dataset. The reason why we don't focus on high-variance is because this symbolizes outliers, which have been dealt with before in data preporcesing. This function call eliminate low-variance (based on threshold) and all cosntant variables (regardless of threshold)
We will start off this analysis eliminating univariate features (i.e: the featuers with constant values)
In [38]:
# default_pipeline.feature_analysis.feature_selection.manual_feature_selection.fit(type="LowVariances",
# threshold=0.5,
# delete_features=True,
# plot=True)
As explained in scikit-learn's in (1.13.1 Feature selection)[https://scikit-learn.org/stable/modules/feature_selection.html], the variance threshold must be selected carefully. Too low may delete few variables, and too may be too restrictive, deleting more variables than it should.
Eliminating highly correlated feature¶
Highly correlated variables (multicolinearity) are problem for models because they introduce a redundancy (features that contain significantly related similar information are not bringing much new insight into the model's input) to the model that can introduce significant variance. This is due to the fact that small changes in the data may make the coefficeints of the highly correlated variables swing more than it should
A note on the shape of this heatmap: due to the high amount of features, and the redundancy to measure the correlation between features (where corr(A,B) = corr(B,A)) we set 'np.triu(np.ones_like(corr, dtype=bool))' in the utilities functions in order to show only new non-redudant correlations between features, thus the right triangle shape. Well, thats a lot to digest! We can see some solid red (high positive correlation) and medium-solid blue (some high negative correlation). Lets use a non-visual methodology to confirm our initial hypothesis. We will use variance inflaction factor (VIF) along with checking manually. A brief explanation on VIF. Formula: $$ VIF_i = \frac{1}{1 - R_i^2} $$ You regress (i.e: do linear regression) on the ith feature as target variable, and all other features as predictors. You then compute the coefficient of determination as a way to measure how well the predictors fit the target variable. VIF values ranges from [1, inf], where lower bound signifies little multicolinearity (R^2 = 0), and upper bound occurs when R^2=1 (perfect multicolineairty). 5 is considered a standard threshold for VIF as it symbolized an 80% R^2. Once you ve obtained the results of VIF, you need to delete the variable with the highest VIF, and recompute VIF until there is no multicolinearity. For example, say there are three features that are 4 features, 3 of them being linear combinations of each other. You would delete the variables with VIF until there is no VIF (when only one of those linear combinations remains). You cant delete all n-1 variables where n are the amount of variables with exceeding (with respect to the chosen threshold), because you may be deleting one that has high VIF due to another feature.
A relevant note on why one-hot-encoding must be done dropping the first one: If we were to not remove one of the labels of one hot encoding, you would be able to predict which level of the categorical variable based on all other ones (there is only degree of freedom for categories levels; they are a linear combination). You would essentially see inf VIF in that area and delete it in this section. The VIF has to be computed every time we delete a feature due to high multicolinearity. Lets do that
In [39]:
#models_pipeline.feature_analysis.feature_selection.manual_feature_selection.fit(type="VIF", threshold=10, delete_features=True, plot=False)
PCA¶
PCA can still bring some more value to feature selection. It will take into account interaction effects by itself and find the principals that capture as much variance as we specify. Thus, its inclusion in the feature selection models_pipeline. It has been excldued due to underperformance.
In [40]:
#models_pipeline.feature_analysis.feature_selection.manual_feature_selection.fit(type="PCA", threshold=.95, delete_features=False, plot=False)
1.3 Automatic Feature Selection¶
L1 regularization¶
Because of L1 regularization being able to set weights 0, we can briefly train our logistic regression model with such regulartization and see which features it uses. The reason why it sets 0 to insignificant feature is because the objective function is not only the MSE but added a component of the wieght magnitude (which is trying to minimize)
In [41]:
# excluded_features, predictive_features, coefficients = models_pipeline.feature_analysis.feature_selection.automatic_feature_selection.fit(type="L1",
# max_iter=1000,
# print_results=True, delete_features=False)
# excluded_features, len(excluded_features)
BORUTA¶
Boruta is a more powerful feature selection method (thus we use it as a reference for variable deletion). It is more powerful that L1 becuase it compares the importance of features to shuffled versions, ensuring robust feature selection
In [42]:
# excluded_features, predictive_features = models_pipeline.feature_analysis.feature_selection.automatic_feature_selection.fit(type="Boruta", max_iter=10, print_results=True, delete_features=False)
# excluded_features, len(excluded_features)
Awesome, lets move onto the actual modelling part!
MODELLING¶
Fitting the model¶
QUESTIONS FOR THIS SECTION
- Can the ROC curve be used for multiclass?
- Can a unsupervised learning algorithm (e.g: KNN) be used for this problem even tough its nature is to be supervised?
- Does val to train deltas are meaningful here?
TO BE DONE FOR THIS SECTION
- Multiple models as classifiers
- Learn more about each alogrithms' paremters
- Can KNN be used here?
Random Forest & Decision Trees¶
Instead of the originally planned logistic regression, we will be using an ensembled model first: random forest (collection of week decision trees). This model is not only likely to outperform the original choice because its nature to handle multiclass better, but also does this several orders of magnitude faster. We will add its not ensembled version too, along with gradient boosted machine Note we are not using not-by-default multiclass classifiers (e.g: logistic regressions, svms)
Non-optimized fitting¶
We first fit all these models with the default paramterers. This is done to constrat more starkly the difference between pre and post tuning.
In [38]:
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import GaussianNB
from library.utils.ownModels.neuralNets.feedForward import FeedForwardNeuralNetwork
from sklearn.linear_model import LogisticRegression
from library.utils.ownModels.majorityClassModel import MajorityClassClassifier
In [39]:
# Ensembled models
gradientBoostingModel = GradientBoostingClassifier()
randomForestModel = RandomForestClassifier()
# Tree-based models
decisionTreeModel = DecisionTreeClassifier()
# Support Vector Machines models
nonLinearSupportVectorModel = SVC()
linearSupportVectorModel = LinearSVC()
# Naive Bayes model
naiveBayesModel = GaussianNB()
# Neural Network model
num_features = default_pipeline.dataset.X_train.shape[1]
num_classes = default_pipeline.dataset.y_train.value_counts().shape[0]
neuralNetworkModel = FeedForwardNeuralNetwork(num_features, num_classes, epochs=2)
# Baseline models
logisticRegressionModel = LogisticRegression()
majorityClassModel = MajorityClassClassifier()
MODEL PERFORMANCE, HYPOTHESIS¶
- Trees (e.g: random forest, decision trees):
- Time to fit:
- Ensemble models (random forest) take more time to train due to the fact that they are larger and heavier than their non-ensembled version.
- Correctness:
- High
- Time to fit:
- Binary-classifiers by default models (e.g: SMV, logistic regression)
- Time to fit:
- compute C models for all C number of classes. Each trained to detect a single class, then when we make predictions, we select the ones that has the highest probability in its predictions ("the most confident in its prediction"). This strategy is called One-vs-Rest, note however, a single logistic regression may be used if we used the softmax objective function (instead of log-odds) (it still heavy computationally, tough). They will be sloder than ensemble models
- Correctness:
- Very low if the problem is non-linear which it is for the SVM and logistic regression
- Time to fit:
In [40]:
pipeline_manager.pipelines
Out[40]:
{'not_baseline': {'ensembled': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'tree_based': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'support_vector_machine': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'naive_bayes': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'feed_forward_neural_network': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>,
'stacking': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>},
'baseline': {'baselines': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>}}
Pipelines always need to diverge from training onwards. Otherwise they will have each other results (which does not follow the isolation pattern we have programmed this with)
In [41]:
# Ensembled models
ensembled_pipeline = pipeline_manager.create_pipeline_divergence(category="not_baseline", pipelineName="ensembled")
# Tree-based models
tree_pipeline = pipeline_manager.create_pipeline_divergence(category="not_baseline", pipelineName="tree_based")
# Support Vector Machines models
supportVectorsMachine_pipeline = pipeline_manager.create_pipeline_divergence(category="not_baseline", pipelineName="support_vector_machine")
# Naive Bayes model
naiveBayes_pipeline = pipeline_manager.create_pipeline_divergence(category="not_baseline", pipelineName="naive_bayes")
# Stacking model
stacking_pipeline = pipeline_manager.create_pipeline_divergence(category="not_baseline", pipelineName="stacking")
# Feed Forward Neural Network model
feedForwardNN_pipeline = pipeline_manager.create_pipeline_divergence(category="not_baseline", pipelineName="feed_forward_neural_network")
# Baseline (we leave the orignal object)
In [42]:
pipeline_manager.pipelines
Out[42]:
{'not_baseline': {'ensembled': <library.pipeline.pipeline.Pipeline at 0x18c2eb5db80>,
'tree_based': <library.pipeline.pipeline.Pipeline at 0x18c2e85ccb0>,
'support_vector_machine': <library.pipeline.pipeline.Pipeline at 0x18c2fbb4aa0>,
'naive_bayes': <library.pipeline.pipeline.Pipeline at 0x18c2fbb6240>,
'feed_forward_neural_network': <library.pipeline.pipeline.Pipeline at 0x18c2f42a930>,
'stacking': <library.pipeline.pipeline.Pipeline at 0x18c2f4293a0>},
'baseline': {'baselines': <library.pipeline.pipeline.Pipeline at 0x18c2c300740>}}
In [43]:
# Ensembled models
ensembled_pipeline.modelling.add_model("Gradient Boosting", gradientBoostingModel)
ensembled_pipeline.modelling.add_model("Random Forest", randomForestModel)
# Tree-based models
tree_pipeline.modelling.add_model("Decision Tree", decisionTreeModel)
# Support Vector Machines models
supportVectorsMachine_pipeline.modelling.add_model("Non-linear Support Vector Machine", nonLinearSupportVectorModel)
supportVectorsMachine_pipeline.modelling.add_model("Linear Support Vector Machine", linearSupportVectorModel)
# Naive Bayes
naiveBayes_pipeline.modelling.add_model("Naive Bayes", naiveBayesModel)
# Stacking model (delayed till models are optimized)
# Feed Forward Neural Network model
feedForwardNN_pipeline.modelling.add_model("Feed Forward Neural Network", neuralNetworkModel, model_type="neural_network")
# Baseline
baseline_pipeline.modelling.add_model("Logistic Regression (baseline)", logisticRegressionModel)
baseline_pipeline.modelling.add_model("Majority Class (baseline)", majorityClassModel)
In [44]:
baseline_pipeline.modelling.list_of_models["Majority Class (baseline)"].tuning_states["pre"].assesment # just an example
Out[44]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Majority Class (baseline)',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': None,
'timeToPredict': None,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': None,
'predictions_train': None,
'predictions_test': None,
'model_sklearn': None}
THIS WILL BE DELETED¶
While we debug, lets exlclude some models we dont need for now (they are very slow to train).
THIS WONT BE DELETED¶
As indicated throughout the notebook this is the part where we eliminate the models that have been excluded from the final training.
| Model | Reason for Exclusion |
|---|---|
| Linear SVM | Ran the training for 1h never converged |
In [45]:
# Ensembled models
ensembled_pipeline.modelling.models_to_exclude = variables["modelling_runner"]["models_to_exclude"]["ensembled"]
# Tree-based models
tree_pipeline.modelling.models_to_exclude = variables["modelling_runner"]["models_to_exclude"]["tree_based"]
# Support Vector Machines models
supportVectorsMachine_pipeline.modelling.models_to_exclude = variables["modelling_runner"]["models_to_exclude"]["support_vector_machine"]
# Naive Bayes models
naiveBayes_pipeline.modelling.models_to_exclude = variables["modelling_runner"]["models_to_exclude"]["naive_bayes"]
# Feed Forward Neural Network model
feedForwardNN_pipeline.modelling.models_to_exclude = variables["modelling_runner"]["models_to_exclude"]["feed_forward_neural_network"]
# Baseline models
baseline_pipeline.modelling.models_to_exclude = variables["modelling_runner"]["models_to_exclude"]["baselines"]
# Stacking model
stacking_pipeline.modelling.models_to_exclude = variables["modelling_runner"]["models_to_exclude"]["stacking"]
Lets finally start fitting the models:
In [46]:
pipeline_manager.pipeline_state = "pre"
In [47]:
pipeline_manager.all_pipelines_execute(methodName="modelling.fit_models",
verbose=False,
exclude_pipeline_names=["stacking"],
current_phase=pipeline_manager.pipeline_state)
Gonna start fitting models in pre phase Gonna start fitting models in pre phase Gonna start fitting models in pre phase Gonna start fitting models in pre phase Gonna start fitting models in pre phase Skipping pipeline stacking in category not_baseline because it is in the exclude list Gonna start fitting models in pre phase
Out[47]:
{'not_baseline': {'ensembled': None,
'support_vector_machine': None,
'naive_bayes': None,
'tree_based': None,
'feed_forward_neural_network': None},
'baseline': {'baselines': None}}
In [48]:
estimators = []
for pipelineName, pipelineObject in pipeline_manager.pipelines["not_baseline"].items():
for modelName, modelObject in pipelineObject.modelling.list_of_models.items():
if modelName in pipelineObject.modelling.models_to_exclude:
continue
modelSklearn = modelObject.tuning_states["pre"].assesment["model_sklearn"]
estimators.append((modelName, modelSklearn))
estimators
Out[48]:
[('Decision Tree', DecisionTreeClassifier()),
('Naive Bayes', GaussianNB()),
('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['relu', 'relu', 'relu', 'relu'],
batch_size=128, epochs=2,
kernel_initializer='glorot_uniform',
learning_rate=0.001, n_layers=4, num_classes=12,
num_features=142, units_per_layer=[512, 256, 128, 64]))]
In [49]:
from sklearn.ensemble import StackingClassifier
In [50]:
#Stacking model
stackingModel = StackingClassifier(
estimators=estimators,
final_estimator=LogisticRegression(),
cv=5,
verbose=3
)
In [51]:
X_train = pipeline_manager.pipelines["not_baseline"]["stacking"].dataset.X_train
y_train = pipeline_manager.pipelines["not_baseline"]["stacking"].dataset.y_train
X_val = pipeline_manager.pipelines["not_baseline"]["stacking"].dataset.X_val
In [52]:
stacking_pipeline.modelling.add_model("Stacking", stackingModel, model_type="stacking")
In [53]:
all_pipelines_to_exclude = []
for pipelineName, pipelineObject in pipeline_manager.pipelines["not_baseline"].items():
if pipelineName == "stacking":
continue
all_pipelines_to_exclude.append(pipelineName)
all_pipelines_to_exclude
Out[53]:
['ensembled', 'tree_based', 'support_vector_machine', 'naive_bayes', 'feed_forward_neural_network']
Stacking currently not implemented
In [54]:
pipeline_manager.all_pipelines_execute(methodName="modelling.fit_models",
current_phase="pre",
exclude_category="baseline",
exclude_pipeline_names=all_pipelines_to_exclude
)
Skipping pipeline ensembled in category not_baseline because it is in the exclude list Skipping pipeline tree_based in category not_baseline because it is in the exclude list Skipping pipeline support_vector_machine in category not_baseline because it is in the exclude list Skipping pipeline naive_bayes in category not_baseline because it is in the exclude list Skipping pipeline feed_forward_neural_network in category not_baseline because it is in the exclude list Gonna start fitting models in pre phase
Out[54]:
{'not_baseline': {'stacking': None}}
Let's make sure the predictions vary between holdout sets
the aforeshown diagram was originally done with the sole intention to debug an error that made predictions be the same across sets. Insights may not be much meaningful after correctio, but it is worth keeping until the end of the notebook development
PREDICTIONS RESULTS¶
Before we get into the actual results, lets elaborate briefly on all the metrics that we are using to asses our classifiers:
- Accuracy => total correctly predicted elemetnts (sigma over the moments we predicted x_i and it was actually x_i / number_of_samples) $$ \text{Accuracy} = \frac{\sum_{i} \mathbf{1}(\hat{y}_i = y_i)}{N} $$
- Precision => out of how many predicted for that class were actually from that class (predicted for class x when it was x/ predicted for class x when it was x + predicted for class x when it was NOT x) $$ \text{Precision}_x = \frac{\text{TP}_x}{\text{TP}_x + \text{FP}_x} $$
- Recall => out of all cases that were positive how many got predicted correctly? $$ \text{Recall}_x = \frac{\text{TP}_x}{\text{TP}_x + \text{FN}_x} $$
- F1-score => harmonic mean of precision and recall (balances both metrics, heavily penalize spreadness between ratios that are being averaged out) $$ \text{F1}_x = 2 \times \frac{\text{Precision}_x \times \text{Recall}_x}{\text{Precision}_x + \text{Recall}_x} $$
- Support => number of actual occurences of class in the dataset
- macro avg => averages given metric across all classes $$ \text{Macro Avg} = \frac{1}{C} \sum_{i=1}^{C} M_i $$
- weighted avg => averages with weights per class occurence (considers frequency of class in average computation) $$ \text{Weighted Avg} = \sum_{i=1}^{C} \frac{\text{Support}_i}{\text{Total Instances}} \times M_i $$
In [55]:
comments = "I know cate will not like this comment"
In [56]:
pipeline_manager.pipelines["not_baseline"]["feed_forward_neural_network"].modelling.list_of_models["Feed Forward Neural Network"].tuning_states["pre"].assesment
Out[56]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Feed Forward Neural Network',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': 9.752876996994019,
'timeToPredict': 3.469944953918457,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': array([7, 7, 0, ..., 0, 7, 0]),
'predictions_train': array([0, 0, 0, ..., 0, 0, 5]),
'predictions_test': None,
'model_sklearn': FeedForwardNeuralNetwork(activations=['relu', 'relu', 'relu', 'relu'],
batch_size=128, epochs=2,
kernel_initializer='glorot_uniform',
learning_rate=0.001, n_layers=4, num_classes=12,
num_features=142, units_per_layer=[512, 256, 128, 64])}
In [57]:
pipeline_manager.pipelines["not_baseline"]["tree_based"].modelling.list_of_models["Decision Tree"].tuning_states["pre"].assesment
Out[57]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Decision Tree',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': 6.0774245262146,
'timeToPredict': 0.06400084495544434,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': array([5, 7, 5, ..., 0, 7, 7]),
'predictions_train': array([0, 0, 7, ..., 3, 5, 5]),
'predictions_test': None,
'model_sklearn': DecisionTreeClassifier()}
In [58]:
model_results = pipeline_manager.all_pipelines_execute(methodName="modelling.evaluate_and_store_models",
verbose=False,
comments=comments,
current_phase=pipeline_manager.pipeline_state)
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Naive Bayes', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 0.28357458114624023, 'timeToPredict': 1.309915542602539, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': array([4, 4, 6, ..., 4, 6, 6]), 'predictions_train': array([4, 6, 4, ..., 1, 4, 4]), 'predictions_test': None, 'model_sklearn': GaussianNB(), 'metrics': {'base_metrics': {'accuracy': 0.1807357006166277, 'precision': 0.4509437493190124, 'recall': 0.1807357006166277, 'f1-score': 0.17162393693884967}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.11043739066804215)}, 'train': {'kappa_train': np.float64(0.11018994650852587)}}}}METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Decision Tree', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 6.0774245262146, 'timeToPredict': 0.06400084495544434, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': array([5, 7, 5, ..., 0, 7, 7]), 'predictions_train': array([0, 0, 7, ..., 3, 5, 5]), 'predictions_test': None, 'model_sklearn': DecisionTreeClassifier(), 'metrics': {'base_metrics': {'accuracy': 0.816500106315118, 'precision': 0.8172926879036424, 'recall': 0.816500106315118, 'f1-score': 0.8166839564481745}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.7739365836565849)}, 'train': {'kappa_train': np.float64(1.0)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
****WARNING****: A model with the same values already exists in the results. Results will not be saved.
You tried to write {'id': '94e3f79e86b3440af7b877b13b51f00fdc0ec0082275ffc9702cc1256214981a', 'timeStamp': '2025-05-12 10:44:51', 'comments': 'UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME', 'modelName': 'Naive Bayes', 'currentPhase': 'pre', 'features_used': ['Memory_PssTotal', 'Memory_PssClean', 'Memory_SharedDirty', 'Memory_PrivateDirty', 'Memory_SharedClean', 'Memory_PrivateClean', 'Memory_SwapPssDirty', 'Memory_HeapSize', 'Memory_HeapAlloc', 'Memory_HeapFree', 'Memory_Views', 'Memory_ViewRootImpl', 'Memory_AppContexts', 'Memory_Activities', 'Memory_Assets', 'Memory_AssetManagers', 'Memory_LocalBinders', 'Memory_ProxyBinders', 'Memory_ParcelMemory', 'Memory_ParcelCount', 'Memory_DeathRecipients', 'Memory_OpenSSLSockets', 'Memory_WebViews', 'API_Process_android.os.Process_start', 'API_Process_android.app.ActivityManager_killBackgroundProcesses', 'API_Process_android.os.Process_killProcess', 'API_Command_java.lang.Runtime_exec', 'API_Command_java.lang.ProcessBuilder_start', 'API_JavaNativeInterface_java.lang.Runtime_loadLibrary', 'API_JavaNativeInterface_java.lang.Runtime_load', 'API_WebView_android.webkit.WebView_loadUrl', 'API_WebView_android.webkit.WebView_loadData', 'API_WebView_android.webkit.WebView_loadDataWithBaseURL', 'API_WebView_android.webkit.WebView_addJavascriptInterface', 'API_WebView_android.webkit.WebView_evaluateJavascript', 'API_WebView_android.webkit.WebView_postUrl', 'API_WebView_android.webkit.WebView_postWebMessage', 'API_WebView_android.webkit.WebView_savePassword', 'API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled', 'API_FileIO_libcore.io.IoBridge_open', 'API_FileIO_android.content.ContextWrapper_openFileInput', 'API_FileIO_android.content.ContextWrapper_openFileOutput', 'API_FileIO_android.content.ContextWrapper_deleteFile', 'API_Database_android.content.ContextWrapper_openOrCreateDatabase', 'API_Database_android.content.ContextWrapper_databaseList', 'API_Database_android.content.ContextWrapper_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_execSQL', 'API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_getPath', 'API_Database_android.database.sqlite.SQLiteDatabase_insert', 'API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow', 'API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_openDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_query', 'API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQuery', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_update', 'API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_compileStatement', 'API_Database_android.database.sqlite.SQLiteDatabase_create', 'API_IPC_android.content.ContextWrapper_sendBroadcast', 'API_IPC_android.content.ContextWrapper_sendStickyBroadcast', 'API_IPC_android.content.ContextWrapper_startActivity', 'API_IPC_android.content.ContextWrapper_startService', 'API_IPC_android.content.ContextWrapper_stopService', 'API_IPC_android.content.ContextWrapper_registerReceiver', 'API_Binder_android.app.ContextImpl_registerReceiver', 'API_Binder_android.app.ActivityThread_handleReceiver', 'API_Binder_android.app.Activity_startActivity', 'API_Crypto_javax.crypto.spec.SecretKeySpec_$init', 'API_Crypto_javax.crypto.Cipher_doFinal', 'API_Crypto-Hash_java.security.MessageDigest_digest', 'API_Crypto-Hash_java.security.MessageDigest_update', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId', 'API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName', 'API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID', 'API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion', 'API_DeviceInfo_android.os.Debug_isDebuggerConnected', 'API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages', 'API_Network_java.net.URL_openConnection', 'API_Network_org.apache.http.impl.client.AbstractHttpClient_execute', 'API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream', 'API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary', 'API_DexClassLoader_dalvik.system.DexFile_loadDex', 'API_DexClassLoader_dalvik.system.DexFile_loadClass', 'API_DexClassLoader_dalvik.system.DexClassLoader_$init', 'API_Base64_android.util.Base64_decode', 'API_Base64_android.util.Base64_encode', 'API_Base64_android.util.Base64_encodeToString', 'API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting', 'API_SystemManager_android.app.NotificationManager_notify', 'API_SystemManager_android.telephony.TelephonyManager_listen', 'API_SystemManager_android.content.BroadcastReceiver_abortBroadcast', 'API_SMS_android.telephony.SmsManager_sendTextMessage', 'API_SMS_android.telephony.SmsManager_sendMultipartTextMessage', 'API_DeviceData_android.content.ContentResolver_query', 'API_DeviceData_android.content.ContentResolver_registerContentObserver', 'API_DeviceData_android.content.ContentResolver_insert', 'API_DeviceData_android.content.ContentResolver_delete', 'API_DeviceData_android.accounts.AccountManager_getAccountsByType', 'API_DeviceData_android.accounts.AccountManager_getAccounts', 'API_DeviceData_android.location.Location_getLatitude', 'API_DeviceData_android.location.Location_getLongitude', 'API_DeviceData_android.media.AudioRecord_startRecording', 'API_DeviceData_android.media.MediaRecorder_start', 'API_DeviceData_android.os.SystemProperties_get', 'API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages', 'API__sessions', 'Network_TotalReceivedBytes', 'Network_TotalReceivedPackets', 'Network_TotalTransmittedBytes', 'Network_TotalTransmittedPackets', 'Battery_wakelock', 'Battery_service', 'Logcat_info', 'Logcat_error', 'Logcat_warning', 'Logcat_debug', 'Logcat_verbose', 'Logcat_total', 'Process_total', 'Reboot_before'], 'hyperParameters': "{'priors': None, 'var_smoothing': 1e-09}", 'timeToFit': 0.28357458114624023, 'timeToPredict': 1.309915542602539, 'accuracy_val': 0.1807357006166277, 'accuracy_test': -1, 'precision_val': 0.4509437493190124, 'precision_test': -1, 'recall_val': 0.1807357006166277, 'recall_test': -1, 'f1-score_val': 0.17162393693884967, 'f1-score_test': -1, 'kappa_val': np.float64(0.11043739066804215), 'kappa_test': -1}
****WARNING****: A model with the same values already exists in the results. Results will not be saved.
You tried to write {'id': 'e9eb10fb5bc1c54f16b7b4ddd18ba40a975f64710132e2635bf65179c8db37e5', 'timeStamp': '2025-05-12 10:44:51', 'comments': 'UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME', 'modelName': 'Decision Tree', 'currentPhase': 'pre', 'features_used': ['Memory_PssTotal', 'Memory_PssClean', 'Memory_SharedDirty', 'Memory_PrivateDirty', 'Memory_SharedClean', 'Memory_PrivateClean', 'Memory_SwapPssDirty', 'Memory_HeapSize', 'Memory_HeapAlloc', 'Memory_HeapFree', 'Memory_Views', 'Memory_ViewRootImpl', 'Memory_AppContexts', 'Memory_Activities', 'Memory_Assets', 'Memory_AssetManagers', 'Memory_LocalBinders', 'Memory_ProxyBinders', 'Memory_ParcelMemory', 'Memory_ParcelCount', 'Memory_DeathRecipients', 'Memory_OpenSSLSockets', 'Memory_WebViews', 'API_Process_android.os.Process_start', 'API_Process_android.app.ActivityManager_killBackgroundProcesses', 'API_Process_android.os.Process_killProcess', 'API_Command_java.lang.Runtime_exec', 'API_Command_java.lang.ProcessBuilder_start', 'API_JavaNativeInterface_java.lang.Runtime_loadLibrary', 'API_JavaNativeInterface_java.lang.Runtime_load', 'API_WebView_android.webkit.WebView_loadUrl', 'API_WebView_android.webkit.WebView_loadData', 'API_WebView_android.webkit.WebView_loadDataWithBaseURL', 'API_WebView_android.webkit.WebView_addJavascriptInterface', 'API_WebView_android.webkit.WebView_evaluateJavascript', 'API_WebView_android.webkit.WebView_postUrl', 'API_WebView_android.webkit.WebView_postWebMessage', 'API_WebView_android.webkit.WebView_savePassword', 'API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled', 'API_FileIO_libcore.io.IoBridge_open', 'API_FileIO_android.content.ContextWrapper_openFileInput', 'API_FileIO_android.content.ContextWrapper_openFileOutput', 'API_FileIO_android.content.ContextWrapper_deleteFile', 'API_Database_android.content.ContextWrapper_openOrCreateDatabase', 'API_Database_android.content.ContextWrapper_databaseList', 'API_Database_android.content.ContextWrapper_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_execSQL', 'API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_getPath', 'API_Database_android.database.sqlite.SQLiteDatabase_insert', 'API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow', 'API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_openDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_query', 'API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQuery', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_update', 'API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_compileStatement', 'API_Database_android.database.sqlite.SQLiteDatabase_create', 'API_IPC_android.content.ContextWrapper_sendBroadcast', 'API_IPC_android.content.ContextWrapper_sendStickyBroadcast', 'API_IPC_android.content.ContextWrapper_startActivity', 'API_IPC_android.content.ContextWrapper_startService', 'API_IPC_android.content.ContextWrapper_stopService', 'API_IPC_android.content.ContextWrapper_registerReceiver', 'API_Binder_android.app.ContextImpl_registerReceiver', 'API_Binder_android.app.ActivityThread_handleReceiver', 'API_Binder_android.app.Activity_startActivity', 'API_Crypto_javax.crypto.spec.SecretKeySpec_$init', 'API_Crypto_javax.crypto.Cipher_doFinal', 'API_Crypto-Hash_java.security.MessageDigest_digest', 'API_Crypto-Hash_java.security.MessageDigest_update', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId', 'API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName', 'API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID', 'API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion', 'API_DeviceInfo_android.os.Debug_isDebuggerConnected', 'API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages', 'API_Network_java.net.URL_openConnection', 'API_Network_org.apache.http.impl.client.AbstractHttpClient_execute', 'API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream', 'API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary', 'API_DexClassLoader_dalvik.system.DexFile_loadDex', 'API_DexClassLoader_dalvik.system.DexFile_loadClass', 'API_DexClassLoader_dalvik.system.DexClassLoader_$init', 'API_Base64_android.util.Base64_decode', 'API_Base64_android.util.Base64_encode', 'API_Base64_android.util.Base64_encodeToString', 'API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting', 'API_SystemManager_android.app.NotificationManager_notify', 'API_SystemManager_android.telephony.TelephonyManager_listen', 'API_SystemManager_android.content.BroadcastReceiver_abortBroadcast', 'API_SMS_android.telephony.SmsManager_sendTextMessage', 'API_SMS_android.telephony.SmsManager_sendMultipartTextMessage', 'API_DeviceData_android.content.ContentResolver_query', 'API_DeviceData_android.content.ContentResolver_registerContentObserver', 'API_DeviceData_android.content.ContentResolver_insert', 'API_DeviceData_android.content.ContentResolver_delete', 'API_DeviceData_android.accounts.AccountManager_getAccountsByType', 'API_DeviceData_android.accounts.AccountManager_getAccounts', 'API_DeviceData_android.location.Location_getLatitude', 'API_DeviceData_android.location.Location_getLongitude', 'API_DeviceData_android.media.AudioRecord_startRecording', 'API_DeviceData_android.media.MediaRecorder_start', 'API_DeviceData_android.os.SystemProperties_get', 'API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages', 'API__sessions', 'Network_TotalReceivedBytes', 'Network_TotalReceivedPackets', 'Network_TotalTransmittedBytes', 'Network_TotalTransmittedPackets', 'Battery_wakelock', 'Battery_service', 'Logcat_info', 'Logcat_error', 'Logcat_warning', 'Logcat_debug', 'Logcat_verbose', 'Logcat_total', 'Process_total', 'Reboot_before'], 'hyperParameters': "{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'monotonic_cst': None, 'random_state': None, 'splitter': 'best'}", 'timeToFit': 6.0774245262146, 'timeToPredict': 0.06400084495544434, 'accuracy_val': 0.816500106315118, 'accuracy_test': -1, 'precision_val': 0.8172926879036424, 'precision_test': -1, 'recall_val': 0.816500106315118, 'recall_test': -1, 'f1-score_val': 0.8166839564481745, 'f1-score_test': -1, 'kappa_val': np.float64(0.7739365836565849), 'kappa_test': -1}
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Majority Class (baseline)', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 0.006997346878051758, 'timeToPredict': 0.0009980201721191406, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': [np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), 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np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5)], 'predictions_test': None, 'model_sklearn': MajorityClassClassifier(), 'metrics': {'base_metrics': {'accuracy': 0.30065915373166063, 'precision': 0.09039592672263835, 'recall': 0.30065915373166063, 'f1-score': 0.13900017766113068}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.0)}, 'train': {'kappa_train': np.float64(0.0)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
****WARNING****: A model with the same values already exists in the results. Results will not be saved.
You tried to write {'id': '4befebefd4f4f643dc75a7afad2b5e224a9bcf5a19eefab966ac17ee131036a7', 'timeStamp': '2025-05-12 10:44:53', 'comments': 'UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME', 'modelName': 'Majority Class (baseline)', 'currentPhase': 'pre', 'features_used': ['Memory_PssTotal', 'Memory_PssClean', 'Memory_SharedDirty', 'Memory_PrivateDirty', 'Memory_SharedClean', 'Memory_PrivateClean', 'Memory_SwapPssDirty', 'Memory_HeapSize', 'Memory_HeapAlloc', 'Memory_HeapFree', 'Memory_Views', 'Memory_ViewRootImpl', 'Memory_AppContexts', 'Memory_Activities', 'Memory_Assets', 'Memory_AssetManagers', 'Memory_LocalBinders', 'Memory_ProxyBinders', 'Memory_ParcelMemory', 'Memory_ParcelCount', 'Memory_DeathRecipients', 'Memory_OpenSSLSockets', 'Memory_WebViews', 'API_Process_android.os.Process_start', 'API_Process_android.app.ActivityManager_killBackgroundProcesses', 'API_Process_android.os.Process_killProcess', 'API_Command_java.lang.Runtime_exec', 'API_Command_java.lang.ProcessBuilder_start', 'API_JavaNativeInterface_java.lang.Runtime_loadLibrary', 'API_JavaNativeInterface_java.lang.Runtime_load', 'API_WebView_android.webkit.WebView_loadUrl', 'API_WebView_android.webkit.WebView_loadData', 'API_WebView_android.webkit.WebView_loadDataWithBaseURL', 'API_WebView_android.webkit.WebView_addJavascriptInterface', 'API_WebView_android.webkit.WebView_evaluateJavascript', 'API_WebView_android.webkit.WebView_postUrl', 'API_WebView_android.webkit.WebView_postWebMessage', 'API_WebView_android.webkit.WebView_savePassword', 'API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled', 'API_FileIO_libcore.io.IoBridge_open', 'API_FileIO_android.content.ContextWrapper_openFileInput', 'API_FileIO_android.content.ContextWrapper_openFileOutput', 'API_FileIO_android.content.ContextWrapper_deleteFile', 'API_Database_android.content.ContextWrapper_openOrCreateDatabase', 'API_Database_android.content.ContextWrapper_databaseList', 'API_Database_android.content.ContextWrapper_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_execSQL', 'API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_getPath', 'API_Database_android.database.sqlite.SQLiteDatabase_insert', 'API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow', 'API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_openDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_query', 'API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQuery', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_update', 'API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_compileStatement', 'API_Database_android.database.sqlite.SQLiteDatabase_create', 'API_IPC_android.content.ContextWrapper_sendBroadcast', 'API_IPC_android.content.ContextWrapper_sendStickyBroadcast', 'API_IPC_android.content.ContextWrapper_startActivity', 'API_IPC_android.content.ContextWrapper_startService', 'API_IPC_android.content.ContextWrapper_stopService', 'API_IPC_android.content.ContextWrapper_registerReceiver', 'API_Binder_android.app.ContextImpl_registerReceiver', 'API_Binder_android.app.ActivityThread_handleReceiver', 'API_Binder_android.app.Activity_startActivity', 'API_Crypto_javax.crypto.spec.SecretKeySpec_$init', 'API_Crypto_javax.crypto.Cipher_doFinal', 'API_Crypto-Hash_java.security.MessageDigest_digest', 'API_Crypto-Hash_java.security.MessageDigest_update', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId', 'API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName', 'API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID', 'API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion', 'API_DeviceInfo_android.os.Debug_isDebuggerConnected', 'API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages', 'API_Network_java.net.URL_openConnection', 'API_Network_org.apache.http.impl.client.AbstractHttpClient_execute', 'API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream', 'API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary', 'API_DexClassLoader_dalvik.system.DexFile_loadDex', 'API_DexClassLoader_dalvik.system.DexFile_loadClass', 'API_DexClassLoader_dalvik.system.DexClassLoader_$init', 'API_Base64_android.util.Base64_decode', 'API_Base64_android.util.Base64_encode', 'API_Base64_android.util.Base64_encodeToString', 'API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting', 'API_SystemManager_android.app.NotificationManager_notify', 'API_SystemManager_android.telephony.TelephonyManager_listen', 'API_SystemManager_android.content.BroadcastReceiver_abortBroadcast', 'API_SMS_android.telephony.SmsManager_sendTextMessage', 'API_SMS_android.telephony.SmsManager_sendMultipartTextMessage', 'API_DeviceData_android.content.ContentResolver_query', 'API_DeviceData_android.content.ContentResolver_registerContentObserver', 'API_DeviceData_android.content.ContentResolver_insert', 'API_DeviceData_android.content.ContentResolver_delete', 'API_DeviceData_android.accounts.AccountManager_getAccountsByType', 'API_DeviceData_android.accounts.AccountManager_getAccounts', 'API_DeviceData_android.location.Location_getLatitude', 'API_DeviceData_android.location.Location_getLongitude', 'API_DeviceData_android.media.AudioRecord_startRecording', 'API_DeviceData_android.media.MediaRecorder_start', 'API_DeviceData_android.os.SystemProperties_get', 'API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages', 'API__sessions', 'Network_TotalReceivedBytes', 'Network_TotalReceivedPackets', 'Network_TotalTransmittedBytes', 'Network_TotalTransmittedPackets', 'Battery_wakelock', 'Battery_service', 'Logcat_info', 'Logcat_error', 'Logcat_warning', 'Logcat_debug', 'Logcat_verbose', 'Logcat_total', 'Process_total', 'Reboot_before'], 'hyperParameters': '{}', 'timeToFit': 0.006997346878051758, 'timeToPredict': 0.0009980201721191406, 'accuracy_val': 0.30065915373166063, 'accuracy_test': -1, 'precision_val': 0.09039592672263835, 'precision_test': -1, 'recall_val': 0.30065915373166063, 'recall_test': -1, 'f1-score_val': 0.13900017766113068, 'f1-score_test': -1, 'kappa_val': np.float64(0.0), 'kappa_test': -1}
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Feed Forward Neural Network', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 9.752876996994019, 'timeToPredict': 3.469944953918457, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': array([7, 7, 0, ..., 0, 7, 0]), 'predictions_train': array([0, 0, 0, ..., 0, 0, 5]), 'predictions_test': None, 'model_sklearn': FeedForwardNeuralNetwork(activations=['relu', 'relu', 'relu', 'relu'],
batch_size=128, epochs=2,
kernel_initializer='glorot_uniform',
learning_rate=0.001, n_layers=4, num_classes=12,
num_features=142, units_per_layer=[512, 256, 128, 64]), 'metrics': {'base_metrics': {'accuracy': 0.4507761003614714, 'precision': 0.5617837043998787, 'recall': 0.4507761003614714, 'f1-score': 0.42888327923648767}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.3125587258025927)}, 'train': {'kappa_train': np.float64(0.30589528709307556)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
****WARNING****: A model with the same values already exists in the results. Results will not be saved.
You tried to write {'id': 'bb79e359cc2d4935b7df909de9784a769a595cd240f0147f4d709ced0dc3f1a8', 'timeStamp': '2025-05-12 10:44:54', 'comments': 'UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME', 'modelName': 'Feed Forward Neural Network', 'currentPhase': 'pre', 'features_used': ['Memory_PssTotal', 'Memory_PssClean', 'Memory_SharedDirty', 'Memory_PrivateDirty', 'Memory_SharedClean', 'Memory_PrivateClean', 'Memory_SwapPssDirty', 'Memory_HeapSize', 'Memory_HeapAlloc', 'Memory_HeapFree', 'Memory_Views', 'Memory_ViewRootImpl', 'Memory_AppContexts', 'Memory_Activities', 'Memory_Assets', 'Memory_AssetManagers', 'Memory_LocalBinders', 'Memory_ProxyBinders', 'Memory_ParcelMemory', 'Memory_ParcelCount', 'Memory_DeathRecipients', 'Memory_OpenSSLSockets', 'Memory_WebViews', 'API_Process_android.os.Process_start', 'API_Process_android.app.ActivityManager_killBackgroundProcesses', 'API_Process_android.os.Process_killProcess', 'API_Command_java.lang.Runtime_exec', 'API_Command_java.lang.ProcessBuilder_start', 'API_JavaNativeInterface_java.lang.Runtime_loadLibrary', 'API_JavaNativeInterface_java.lang.Runtime_load', 'API_WebView_android.webkit.WebView_loadUrl', 'API_WebView_android.webkit.WebView_loadData', 'API_WebView_android.webkit.WebView_loadDataWithBaseURL', 'API_WebView_android.webkit.WebView_addJavascriptInterface', 'API_WebView_android.webkit.WebView_evaluateJavascript', 'API_WebView_android.webkit.WebView_postUrl', 'API_WebView_android.webkit.WebView_postWebMessage', 'API_WebView_android.webkit.WebView_savePassword', 'API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled', 'API_FileIO_libcore.io.IoBridge_open', 'API_FileIO_android.content.ContextWrapper_openFileInput', 'API_FileIO_android.content.ContextWrapper_openFileOutput', 'API_FileIO_android.content.ContextWrapper_deleteFile', 'API_Database_android.content.ContextWrapper_openOrCreateDatabase', 'API_Database_android.content.ContextWrapper_databaseList', 'API_Database_android.content.ContextWrapper_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_execSQL', 'API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_getPath', 'API_Database_android.database.sqlite.SQLiteDatabase_insert', 'API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow', 'API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_openDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_query', 'API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQuery', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_update', 'API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_compileStatement', 'API_Database_android.database.sqlite.SQLiteDatabase_create', 'API_IPC_android.content.ContextWrapper_sendBroadcast', 'API_IPC_android.content.ContextWrapper_sendStickyBroadcast', 'API_IPC_android.content.ContextWrapper_startActivity', 'API_IPC_android.content.ContextWrapper_startService', 'API_IPC_android.content.ContextWrapper_stopService', 'API_IPC_android.content.ContextWrapper_registerReceiver', 'API_Binder_android.app.ContextImpl_registerReceiver', 'API_Binder_android.app.ActivityThread_handleReceiver', 'API_Binder_android.app.Activity_startActivity', 'API_Crypto_javax.crypto.spec.SecretKeySpec_$init', 'API_Crypto_javax.crypto.Cipher_doFinal', 'API_Crypto-Hash_java.security.MessageDigest_digest', 'API_Crypto-Hash_java.security.MessageDigest_update', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId', 'API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName', 'API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID', 'API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion', 'API_DeviceInfo_android.os.Debug_isDebuggerConnected', 'API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages', 'API_Network_java.net.URL_openConnection', 'API_Network_org.apache.http.impl.client.AbstractHttpClient_execute', 'API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream', 'API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary', 'API_DexClassLoader_dalvik.system.DexFile_loadDex', 'API_DexClassLoader_dalvik.system.DexFile_loadClass', 'API_DexClassLoader_dalvik.system.DexClassLoader_$init', 'API_Base64_android.util.Base64_decode', 'API_Base64_android.util.Base64_encode', 'API_Base64_android.util.Base64_encodeToString', 'API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting', 'API_SystemManager_android.app.NotificationManager_notify', 'API_SystemManager_android.telephony.TelephonyManager_listen', 'API_SystemManager_android.content.BroadcastReceiver_abortBroadcast', 'API_SMS_android.telephony.SmsManager_sendTextMessage', 'API_SMS_android.telephony.SmsManager_sendMultipartTextMessage', 'API_DeviceData_android.content.ContentResolver_query', 'API_DeviceData_android.content.ContentResolver_registerContentObserver', 'API_DeviceData_android.content.ContentResolver_insert', 'API_DeviceData_android.content.ContentResolver_delete', 'API_DeviceData_android.accounts.AccountManager_getAccountsByType', 'API_DeviceData_android.accounts.AccountManager_getAccounts', 'API_DeviceData_android.location.Location_getLatitude', 'API_DeviceData_android.location.Location_getLongitude', 'API_DeviceData_android.media.AudioRecord_startRecording', 'API_DeviceData_android.media.MediaRecorder_start', 'API_DeviceData_android.os.SystemProperties_get', 'API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages', 'API__sessions', 'Network_TotalReceivedBytes', 'Network_TotalReceivedPackets', 'Network_TotalTransmittedBytes', 'Network_TotalTransmittedPackets', 'Battery_wakelock', 'Battery_service', 'Logcat_info', 'Logcat_error', 'Logcat_warning', 'Logcat_debug', 'Logcat_verbose', 'Logcat_total', 'Process_total', 'Reboot_before'], 'hyperParameters': "{'num_features': 142, 'num_classes': 12, 'batch_size': 128, 'epochs': 2, 'n_layers': 4, 'units_per_layer': [512, 256, 128, 64], 'activations': ['relu', 'relu', 'relu', 'relu'], 'learning_rate': 0.001, 'kernel_initializer': 'glorot_uniform'}", 'timeToFit': 9.752876996994019, 'timeToPredict': 3.469944953918457, 'accuracy_val': 0.4507761003614714, 'accuracy_test': -1, 'precision_val': 0.5617837043998787, 'precision_test': -1, 'recall_val': 0.4507761003614714, 'recall_test': -1, 'f1-score_val': 0.42888327923648767, 'f1-score_test': -1, 'kappa_val': np.float64(0.3125587258025927), 'kappa_test': -1}
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Stacking', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 60.18330192565918, 'timeToPredict': 4.796907901763916, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': array([5, 7, 5, ..., 0, 7, 7]), 'predictions_train': array([0, 0, 7, ..., 3, 5, 5]), 'predictions_test': None, 'model_sklearn': StackingClassifier(cv=5,
estimators=[('Decision Tree', DecisionTreeClassifier()),
('Naive Bayes', GaussianNB()),
('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['relu',
'relu',
'relu',
'relu'],
batch_size=128,
epochs=2,
kernel_initializer='glorot_uniform',
learning_rate=0.001,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[512,
256,
128,
64]))],
final_estimator=LogisticRegression(), verbose=3), 'metrics': {'base_metrics': {'accuracy': 0.8196895598554115, 'precision': 0.8200265676748922, 'recall': 0.8196895598554115, 'f1-score': 0.8197001524283438}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.7777682181688985)}, 'train': {'kappa_train': np.float64(0.9990815757648093)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
****WARNING****: A model with the same values already exists in the results. Results will not be saved.
You tried to write {'id': '4335f91bbb5705ae47f32db6f6c28a6f1dfabda08d7a477e0ddc5d707ac224eb', 'timeStamp': '2025-05-12 10:44:55', 'comments': 'UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME', 'modelName': 'Stacking', 'currentPhase': 'pre', 'features_used': ['Memory_PssTotal', 'Memory_PssClean', 'Memory_SharedDirty', 'Memory_PrivateDirty', 'Memory_SharedClean', 'Memory_PrivateClean', 'Memory_SwapPssDirty', 'Memory_HeapSize', 'Memory_HeapAlloc', 'Memory_HeapFree', 'Memory_Views', 'Memory_ViewRootImpl', 'Memory_AppContexts', 'Memory_Activities', 'Memory_Assets', 'Memory_AssetManagers', 'Memory_LocalBinders', 'Memory_ProxyBinders', 'Memory_ParcelMemory', 'Memory_ParcelCount', 'Memory_DeathRecipients', 'Memory_OpenSSLSockets', 'Memory_WebViews', 'API_Process_android.os.Process_start', 'API_Process_android.app.ActivityManager_killBackgroundProcesses', 'API_Process_android.os.Process_killProcess', 'API_Command_java.lang.Runtime_exec', 'API_Command_java.lang.ProcessBuilder_start', 'API_JavaNativeInterface_java.lang.Runtime_loadLibrary', 'API_JavaNativeInterface_java.lang.Runtime_load', 'API_WebView_android.webkit.WebView_loadUrl', 'API_WebView_android.webkit.WebView_loadData', 'API_WebView_android.webkit.WebView_loadDataWithBaseURL', 'API_WebView_android.webkit.WebView_addJavascriptInterface', 'API_WebView_android.webkit.WebView_evaluateJavascript', 'API_WebView_android.webkit.WebView_postUrl', 'API_WebView_android.webkit.WebView_postWebMessage', 'API_WebView_android.webkit.WebView_savePassword', 'API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled', 'API_FileIO_libcore.io.IoBridge_open', 'API_FileIO_android.content.ContextWrapper_openFileInput', 'API_FileIO_android.content.ContextWrapper_openFileOutput', 'API_FileIO_android.content.ContextWrapper_deleteFile', 'API_Database_android.content.ContextWrapper_openOrCreateDatabase', 'API_Database_android.content.ContextWrapper_databaseList', 'API_Database_android.content.ContextWrapper_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_execSQL', 'API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_getPath', 'API_Database_android.database.sqlite.SQLiteDatabase_insert', 'API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow', 'API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_openDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_query', 'API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQuery', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_update', 'API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_compileStatement', 'API_Database_android.database.sqlite.SQLiteDatabase_create', 'API_IPC_android.content.ContextWrapper_sendBroadcast', 'API_IPC_android.content.ContextWrapper_sendStickyBroadcast', 'API_IPC_android.content.ContextWrapper_startActivity', 'API_IPC_android.content.ContextWrapper_startService', 'API_IPC_android.content.ContextWrapper_stopService', 'API_IPC_android.content.ContextWrapper_registerReceiver', 'API_Binder_android.app.ContextImpl_registerReceiver', 'API_Binder_android.app.ActivityThread_handleReceiver', 'API_Binder_android.app.Activity_startActivity', 'API_Crypto_javax.crypto.spec.SecretKeySpec_$init', 'API_Crypto_javax.crypto.Cipher_doFinal', 'API_Crypto-Hash_java.security.MessageDigest_digest', 'API_Crypto-Hash_java.security.MessageDigest_update', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId', 'API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName', 'API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID', 'API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion', 'API_DeviceInfo_android.os.Debug_isDebuggerConnected', 'API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages', 'API_Network_java.net.URL_openConnection', 'API_Network_org.apache.http.impl.client.AbstractHttpClient_execute', 'API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream', 'API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary', 'API_DexClassLoader_dalvik.system.DexFile_loadDex', 'API_DexClassLoader_dalvik.system.DexFile_loadClass', 'API_DexClassLoader_dalvik.system.DexClassLoader_$init', 'API_Base64_android.util.Base64_decode', 'API_Base64_android.util.Base64_encode', 'API_Base64_android.util.Base64_encodeToString', 'API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting', 'API_SystemManager_android.app.NotificationManager_notify', 'API_SystemManager_android.telephony.TelephonyManager_listen', 'API_SystemManager_android.content.BroadcastReceiver_abortBroadcast', 'API_SMS_android.telephony.SmsManager_sendTextMessage', 'API_SMS_android.telephony.SmsManager_sendMultipartTextMessage', 'API_DeviceData_android.content.ContentResolver_query', 'API_DeviceData_android.content.ContentResolver_registerContentObserver', 'API_DeviceData_android.content.ContentResolver_insert', 'API_DeviceData_android.content.ContentResolver_delete', 'API_DeviceData_android.accounts.AccountManager_getAccountsByType', 'API_DeviceData_android.accounts.AccountManager_getAccounts', 'API_DeviceData_android.location.Location_getLatitude', 'API_DeviceData_android.location.Location_getLongitude', 'API_DeviceData_android.media.AudioRecord_startRecording', 'API_DeviceData_android.media.MediaRecorder_start', 'API_DeviceData_android.os.SystemProperties_get', 'API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages', 'API__sessions', 'Network_TotalReceivedBytes', 'Network_TotalReceivedPackets', 'Network_TotalTransmittedBytes', 'Network_TotalTransmittedPackets', 'Battery_wakelock', 'Battery_service', 'Logcat_info', 'Logcat_error', 'Logcat_warning', 'Logcat_debug', 'Logcat_verbose', 'Logcat_total', 'Process_total', 'Reboot_before'], 'hyperParameters': "{'cv': 5, 'estimators': [('Decision Tree', DecisionTreeClassifier()), ('Naive Bayes', GaussianNB()), ('Feed Forward Neural Network', FeedForwardNeuralNetwork(activations=['relu', 'relu', 'relu', 'relu'],\n batch_size=128, epochs=2,\n kernel_initializer='glorot_uniform',\n learning_rate=0.001, n_layers=4, num_classes=12,\n num_features=142, units_per_layer=[512, 256, 128, 64]))], 'final_estimator__C': 1.0, 'final_estimator__class_weight': None, 'final_estimator__dual': False, 'final_estimator__fit_intercept': True, 'final_estimator__intercept_scaling': 1, 'final_estimator__l1_ratio': None, 'final_estimator__max_iter': 100, 'final_estimator__multi_class': 'deprecated', 'final_estimator__n_jobs': None, 'final_estimator__penalty': 'l2', 'final_estimator__random_state': None, 'final_estimator__solver': 'lbfgs', 'final_estimator__tol': 0.0001, 'final_estimator__verbose': 0, 'final_estimator__warm_start': False, 'final_estimator': LogisticRegression(), 'n_jobs': None, 'passthrough': False, 'stack_method': 'auto', 'verbose': 3, 'Decision Tree': DecisionTreeClassifier(), 'Naive Bayes': GaussianNB(), 'Feed Forward Neural Network': FeedForwardNeuralNetwork(activations=['relu', 'relu', 'relu', 'relu'],\n batch_size=128, epochs=2,\n kernel_initializer='glorot_uniform',\n learning_rate=0.001, n_layers=4, num_classes=12,\n num_features=142, units_per_layer=[512, 256, 128, 64]), 'Decision Tree__ccp_alpha': 0.0, 'Decision Tree__class_weight': None, 'Decision Tree__criterion': 'gini', 'Decision Tree__max_depth': None, 'Decision Tree__max_features': None, 'Decision Tree__max_leaf_nodes': None, 'Decision Tree__min_impurity_decrease': 0.0, 'Decision Tree__min_samples_leaf': 1, 'Decision Tree__min_samples_split': 2, 'Decision Tree__min_weight_fraction_leaf': 0.0, 'Decision Tree__monotonic_cst': None, 'Decision Tree__random_state': None, 'Decision Tree__splitter': 'best', 'Naive Bayes__priors': None, 'Naive Bayes__var_smoothing': 1e-09, 'Feed Forward Neural Network__num_features': 142, 'Feed Forward Neural Network__num_classes': 12, 'Feed Forward Neural Network__batch_size': 128, 'Feed Forward Neural Network__epochs': 2, 'Feed Forward Neural Network__n_layers': 4, 'Feed Forward Neural Network__units_per_layer': [512, 256, 128, 64], 'Feed Forward Neural Network__activations': ['relu', 'relu', 'relu', 'relu'], 'Feed Forward Neural Network__learning_rate': 0.001, 'Feed Forward Neural Network__kernel_initializer': 'glorot_uniform'}", 'timeToFit': 60.18330192565918, 'timeToPredict': 4.796907901763916, 'accuracy_val': 0.8196895598554115, 'accuracy_test': -1, 'precision_val': 0.8200265676748922, 'precision_test': -1, 'recall_val': 0.8196895598554115, 'recall_test': -1, 'f1-score_val': 0.8197001524283438, 'f1-score_test': -1, 'kappa_val': np.float64(0.7777682181688985), 'kappa_test': -1}
Performance Evaluation¶
Below are shown all the metrics we can compare our plots to:
In [59]:
pipeline_manager.pipelines["not_baseline"]["stacking"].modelling.list_of_models["Stacking"].tuning_states["pre"].assesment
Out[59]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Stacking',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': 60.18330192565918,
'timeToPredict': 4.796907901763916,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': array([5, 7, 5, ..., 0, 7, 7]),
'predictions_train': array([0, 0, 7, ..., 3, 5, 5]),
'predictions_test': None,
'model_sklearn': StackingClassifier(cv=5,
estimators=[('Decision Tree', DecisionTreeClassifier()),
('Naive Bayes', GaussianNB()),
('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['relu',
'relu',
'relu',
'relu'],
batch_size=128,
epochs=2,
kernel_initializer='glorot_uniform',
learning_rate=0.001,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[512,
256,
128,
64]))],
final_estimator=LogisticRegression(), verbose=3),
'metrics': {'base_metrics': {'accuracy': 0.8196895598554115,
'precision': 0.8200265676748922,
'recall': 0.8196895598554115,
'f1-score': 0.8197001524283438},
'additional_metrics': {'not_train': {'kappa': np.float64(0.7777682181688985)},
'train': {'kappa_train': np.float64(0.9990815757648093)}}}}
Cross-Model Evaluation (pre-tuning)¶
In [60]:
pipeline_manager.pipelines_analysis._compute_classification_report()
Out[60]:
| precision | recall | f1-score | support | accuracy | kappa | precision | recall | f1-score | support | ... | f1-score | support | accuracy | kappa | precision | recall | f1-score | support | accuracy | kappa | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Adware | 0.833648 | 0.829727 | 0.831683 | 1063.0 | 0.8165 | 0.773937 | 0.45614 | 0.048918 | 0.08836 | 1063.0 | ... | 0.832007 | 1063.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 1063.0 | 0.300659 | 0.0 |
| Backdoor | 0.638298 | 0.631579 | 0.634921 | 95.0 | 0.8165 | 0.773937 | 0.063492 | 0.042105 | 0.050633 | 95.0 | ... | 0.617021 | 95.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 95.0 | 0.300659 | 0.0 |
| FileInfector | 0.83871 | 0.702703 | 0.764706 | 37.0 | 0.8165 | 0.773937 | 0.0 | 0.0 | 0.0 | 37.0 | ... | 0.764706 | 37.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 37.0 | 0.300659 | 0.0 |
| PUA | 0.713235 | 0.795082 | 0.751938 | 122.0 | 0.8165 | 0.773937 | 0.542373 | 0.262295 | 0.353591 | 122.0 | ... | 0.762646 | 122.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 122.0 | 0.300659 | 0.0 |
| Ransomware | 0.784257 | 0.772989 | 0.778582 | 348.0 | 0.8165 | 0.773937 | 0.127424 | 0.925287 | 0.224 | 348.0 | ... | 0.778736 | 348.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 348.0 | 0.300659 | 0.0 |
| Riskware | 0.871266 | 0.866337 | 0.868794 | 1414.0 | 0.8165 | 0.773937 | 0.837838 | 0.197313 | 0.319405 | 1414.0 | ... | 0.87265 | 1414.0 | 0.81969 | 0.777768 | 0.300659 | 1.0 | 0.462318 | 1414.0 | 0.300659 | 0.0 |
| Scareware | 0.789474 | 0.697674 | 0.740741 | 86.0 | 0.8165 | 0.773937 | 0.06237 | 0.697674 | 0.114504 | 86.0 | ... | 0.722892 | 86.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 86.0 | 0.300659 | 0.0 |
| Trojan | 0.833526 | 0.836427 | 0.834974 | 862.0 | 0.8165 | 0.773937 | 0.082353 | 0.008121 | 0.014784 | 862.0 | ... | 0.84235 | 862.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 862.0 | 0.300659 | 0.0 |
| Trojan_Banker | 0.48 | 0.48 | 0.48 | 25.0 | 0.8165 | 0.773937 | 0.014388 | 0.08 | 0.02439 | 25.0 | ... | 0.565217 | 25.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 25.0 | 0.300659 | 0.0 |
| Trojan_Dropper | 0.64557 | 0.6375 | 0.641509 | 160.0 | 0.8165 | 0.773937 | 0.4 | 0.0625 | 0.108108 | 160.0 | ... | 0.639752 | 160.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 160.0 | 0.300659 | 0.0 |
| Trojan_SMS | 0.659292 | 0.712919 | 0.685057 | 209.0 | 0.8165 | 0.773937 | 0.685714 | 0.114833 | 0.196721 | 209.0 | ... | 0.696471 | 209.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 209.0 | 0.300659 | 0.0 |
| Trojan_Spy | 0.831579 | 0.840426 | 0.835979 | 282.0 | 0.8165 | 0.773937 | 0.179567 | 0.205674 | 0.191736 | 282.0 | ... | 0.834813 | 282.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 282.0 | 0.300659 | 0.0 |
| macro avg | 0.743238 | 0.733613 | 0.737407 | 4703.0 | 0.8165 | 0.773937 | 0.287638 | 0.220393 | 0.140519 | 4703.0 | ... | 0.744105 | 4703.0 | 0.81969 | 0.777768 | 0.025055 | 0.083333 | 0.038527 | 4703.0 | 0.300659 | 0.0 |
| weighted avg | 0.817293 | 0.8165 | 0.816684 | 4703.0 | 0.8165 | 0.773937 | 0.450944 | 0.180736 | 0.171624 | 4703.0 | ... | 0.8197 | 4703.0 | 0.81969 | 0.777768 | 0.090396 | 0.300659 | 0.139 | 4703.0 | 0.300659 | 0.0 |
| modelName | Decision Tree | Decision Tree | Decision Tree | Decision Tree | Decision Tree | Decision Tree | Naive Bayes | Naive Bayes | Naive Bayes | Naive Bayes | ... | Stacking | Stacking | Stacking | Stacking | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) |
15 rows × 30 columns
In [61]:
pipeline_manager.pipelines_analysis.merged_report_per_phase["pre"]
Out[61]:
| precision | recall | f1-score | support | accuracy | kappa | precision | recall | f1-score | support | ... | f1-score | support | accuracy | kappa | precision | recall | f1-score | support | accuracy | kappa | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Adware | 0.833648 | 0.829727 | 0.831683 | 1063.0 | 0.8165 | 0.773937 | 0.45614 | 0.048918 | 0.08836 | 1063.0 | ... | 0.832007 | 1063.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 1063.0 | 0.300659 | 0.0 |
| Backdoor | 0.638298 | 0.631579 | 0.634921 | 95.0 | 0.8165 | 0.773937 | 0.063492 | 0.042105 | 0.050633 | 95.0 | ... | 0.617021 | 95.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 95.0 | 0.300659 | 0.0 |
| FileInfector | 0.83871 | 0.702703 | 0.764706 | 37.0 | 0.8165 | 0.773937 | 0.0 | 0.0 | 0.0 | 37.0 | ... | 0.764706 | 37.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 37.0 | 0.300659 | 0.0 |
| PUA | 0.713235 | 0.795082 | 0.751938 | 122.0 | 0.8165 | 0.773937 | 0.542373 | 0.262295 | 0.353591 | 122.0 | ... | 0.762646 | 122.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 122.0 | 0.300659 | 0.0 |
| Ransomware | 0.784257 | 0.772989 | 0.778582 | 348.0 | 0.8165 | 0.773937 | 0.127424 | 0.925287 | 0.224 | 348.0 | ... | 0.778736 | 348.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 348.0 | 0.300659 | 0.0 |
| Riskware | 0.871266 | 0.866337 | 0.868794 | 1414.0 | 0.8165 | 0.773937 | 0.837838 | 0.197313 | 0.319405 | 1414.0 | ... | 0.87265 | 1414.0 | 0.81969 | 0.777768 | 0.300659 | 1.0 | 0.462318 | 1414.0 | 0.300659 | 0.0 |
| Scareware | 0.789474 | 0.697674 | 0.740741 | 86.0 | 0.8165 | 0.773937 | 0.06237 | 0.697674 | 0.114504 | 86.0 | ... | 0.722892 | 86.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 86.0 | 0.300659 | 0.0 |
| Trojan | 0.833526 | 0.836427 | 0.834974 | 862.0 | 0.8165 | 0.773937 | 0.082353 | 0.008121 | 0.014784 | 862.0 | ... | 0.84235 | 862.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 862.0 | 0.300659 | 0.0 |
| Trojan_Banker | 0.48 | 0.48 | 0.48 | 25.0 | 0.8165 | 0.773937 | 0.014388 | 0.08 | 0.02439 | 25.0 | ... | 0.565217 | 25.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 25.0 | 0.300659 | 0.0 |
| Trojan_Dropper | 0.64557 | 0.6375 | 0.641509 | 160.0 | 0.8165 | 0.773937 | 0.4 | 0.0625 | 0.108108 | 160.0 | ... | 0.639752 | 160.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 160.0 | 0.300659 | 0.0 |
| Trojan_SMS | 0.659292 | 0.712919 | 0.685057 | 209.0 | 0.8165 | 0.773937 | 0.685714 | 0.114833 | 0.196721 | 209.0 | ... | 0.696471 | 209.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 209.0 | 0.300659 | 0.0 |
| Trojan_Spy | 0.831579 | 0.840426 | 0.835979 | 282.0 | 0.8165 | 0.773937 | 0.179567 | 0.205674 | 0.191736 | 282.0 | ... | 0.834813 | 282.0 | 0.81969 | 0.777768 | 0.0 | 0.0 | 0.0 | 282.0 | 0.300659 | 0.0 |
| macro avg | 0.743238 | 0.733613 | 0.737407 | 4703.0 | 0.8165 | 0.773937 | 0.287638 | 0.220393 | 0.140519 | 4703.0 | ... | 0.744105 | 4703.0 | 0.81969 | 0.777768 | 0.025055 | 0.083333 | 0.038527 | 4703.0 | 0.300659 | 0.0 |
| weighted avg | 0.817293 | 0.8165 | 0.816684 | 4703.0 | 0.8165 | 0.773937 | 0.450944 | 0.180736 | 0.171624 | 4703.0 | ... | 0.8197 | 4703.0 | 0.81969 | 0.777768 | 0.090396 | 0.300659 | 0.139 | 4703.0 | 0.300659 | 0.0 |
| modelName | Decision Tree | Decision Tree | Decision Tree | Decision Tree | Decision Tree | Decision Tree | Naive Bayes | Naive Bayes | Naive Bayes | Naive Bayes | ... | Stacking | Stacking | Stacking | Stacking | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) |
15 rows × 30 columns
In [62]:
pipeline_manager.pipelines_analysis.plot_cross_model_comparison(save_plots=False, save_path="")
PLOTTING CROSS MODEL COMPARISON FOR pre PHASE Plotting: accuracy Plotting: precision Plotting: recall Plotting: f1-score Plotting: kappa
c:\Users\irina\ml-final-project\library\pipeline\analysis\pipelines_analysis.py:197: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() c:\Users\irina\ml-final-project\library\pipeline\analysis\pipelines_analysis.py:197: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend()
In [63]:
metrics_df = pipeline_manager.pipelines_analysis.plot_results_df(metrics=["timeToFit", "timeToPredict"], save_plots=False, save_path="")
metrics_df
Metrics df: id timeStamp \
0 e9eb10fb5bc1c54f16b7b4ddd18ba40a975f64710132e2... 2025-05-12 10:44:51
comments modelName currentPhase \
0 UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME Decision Tree pre
features_used \
0 [Memory_PssTotal, Memory_PssClean, Memory_Shar...
hyperParameters timeToFit \
0 {'ccp_alpha': 0.0, 'class_weight': None, 'crit... 6.077425
timeToPredict accuracy_val accuracy_test precision_val precision_test \
0 0.064001 0.8165 -1 0.817293 -1
recall_val recall_test f1-score_val f1-score_test kappa_val kappa_test
0 0.8165 -1 0.816684 -1 0.773937 -1
c:\Users\irina\ml-final-project\library\pipeline\analysis\pipelines_analysis.py:291: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=metrics_df, x='modelName', y=metric, ax=ax, palette="viridis") c:\Users\irina\ml-final-project\library\pipeline\analysis\pipelines_analysis.py:291: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=metrics_df, x='modelName', y=metric, ax=ax, palette="viridis")
Out[63]:
| id | timeStamp | comments | modelName | currentPhase | features_used | hyperParameters | timeToFit | timeToPredict | accuracy_val | accuracy_test | precision_val | precision_test | recall_val | recall_test | f1-score_val | f1-score_test | kappa_val | kappa_test | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | e9eb10fb5bc1c54f16b7b4ddd18ba40a975f64710132e2... | 2025-05-12 10:44:51 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Decision Tree | pre | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {'ccp_alpha': 0.0, 'class_weight': None, 'crit... | 6.077425 | 0.064001 | 0.816500 | -1 | 0.817293 | -1 | 0.816500 | -1 | 0.816684 | -1 | 0.773937 | -1 |
| 0 | 94e3f79e86b3440af7b877b13b51f00fdc0ec0082275ff... | 2025-05-12 10:44:51 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Naive Bayes | pre | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {'priors': None, 'var_smoothing': 1e-09} | 0.283575 | 1.309916 | 0.180736 | -1 | 0.450944 | -1 | 0.180736 | -1 | 0.171624 | -1 | 0.110437 | -1 |
| 0 | bb79e359cc2d4935b7df909de9784a769a595cd240f014... | 2025-05-12 10:44:54 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Feed Forward Neural Network | pre | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {'num_features': 142, 'num_classes': 12, 'batc... | 9.752877 | 3.469945 | 0.450776 | -1 | 0.561784 | -1 | 0.450776 | -1 | 0.428883 | -1 | 0.312559 | -1 |
| 0 | 4335f91bbb5705ae47f32db6f6c28a6f1dfabda08d7a47... | 2025-05-12 10:44:55 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Stacking | pre | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {'cv': 5, 'estimators': [('Decision Tree', Dec... | 60.183302 | 4.796908 | 0.819690 | -1 | 0.820027 | -1 | 0.819690 | -1 | 0.819700 | -1 | 0.777768 | -1 |
| 0 | 4befebefd4f4f643dc75a7afad2b5e224a9bcf5a19eefa... | 2025-05-12 10:44:53 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Majority Class (baseline) | pre | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {} | 0.006997 | 0.000998 | 0.300659 | -1 | 0.090396 | -1 | 0.300659 | -1 | 0.139000 | -1 | 0.000000 | -1 |
Results Summary Visualization¶
In [64]:
pipeline_manager.pipelines_analysis.plot_results_summary(training_metric="timeToFit", performance_metric="kappa", save_plots=False, save_path="")
Intra-model Evaluation (pre-tuning)¶
We plot 3 metrics (f1, recall and precision) for each model constrasting its performance between trainign and validation. It is not subject of interest cross-model evaluation, but rather, intra-model evaluation.
In [65]:
pipeline_manager.pipelines["not_baseline"]["tree_based"].modelling.list_of_models["Decision Tree"].tuning_states["pre"].assesment
Out[65]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Decision Tree',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': 6.0774245262146,
'timeToPredict': 0.06400084495544434,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': array([5, 7, 5, ..., 0, 7, 7]),
'predictions_train': array([0, 0, 7, ..., 3, 5, 5]),
'predictions_test': None,
'model_sklearn': DecisionTreeClassifier(),
'metrics': {'base_metrics': {'accuracy': 0.816500106315118,
'precision': 0.8172926879036424,
'recall': 0.816500106315118,
'f1-score': 0.8166839564481745},
'additional_metrics': {'not_train': {'kappa': np.float64(0.7739365836565849)},
'train': {'kappa_train': np.float64(1.0)}}}}
In [66]:
pipeline_manager.pipelines_analysis.plot_intra_model_comparison(save_plots=False, save_path="")
METRICS IS None
c:\Users\irina\ml-final-project\library\pipeline\analysis\pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() c:\Users\irina\ml-final-project\library\pipeline\analysis\pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() c:\Users\irina\ml-final-project\library\pipeline\analysis\pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() c:\Users\irina\ml-final-project\library\pipeline\analysis\pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() c:\Users\irina\ml-final-project\library\pipeline\analysis\pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend()
We can also observe the progress per epoch so lets visualize that:
In [70]:
#pipeline_manager.pipelines["not_baseline"]["feed_forward_neural_network"].modelling.list_of_models["Feed Forward Neural Network"].tuning_states["pre"].assesment["model_sklearn"].history.history
In [71]:
#pipeline_manager.pipelines_analysis.plot_per_epoch_progress(metrics=["accuracy", "loss"], save_plots=False, save_path="")
Residual analysis (pre-tuning)¶
This contains the confusion matrices (weighted and not weighted). It also returns the specific elements that were erroneously classified. The analysis of this residuals may be meaningful for further inspection (clustering analysis, EDA...)
In [67]:
residuals, confusion_matrices = pipeline_manager.pipelines_analysis.plot_confusion_matrix(save_plots=False, save_path="")
residuals
Plotting: Decision Tree Plotting: Naive Bayes Plotting: Feed Forward Neural Network Plotting: Stacking Plotting: Majority Class (baseline)
Out[67]:
{'tree_based': 53099 3
42114 11
41946 10
12505 0
22423 5
..
2906 0
33099 7
52857 3
47610 7
36519 5
Length: 863, dtype: int64,
'naive_bayes': 53099 3
30660 7
24323 5
11712 0
52498 3
..
44394 7
7212 0
7612 0
46313 7
36519 5
Length: 3853, dtype: int64,
'feed_forward_neural_network': 53099 3
24323 5
52498 3
5763 0
34371 5
..
17232 9
52857 3
47610 7
7212 0
36519 5
Length: 2583, dtype: int64,
'stacking': 53099 3
42114 11
41946 10
12505 0
22423 5
..
49826 4
2906 0
33099 7
47610 7
36519 5
Length: 848, dtype: int64,
'baselines': 53099 3
30660 7
11712 0
52498 3
5763 0
..
47610 7
44394 7
7212 0
7612 0
46313 7
Length: 3289, dtype: int64}
Feature importances (pre-tuning)¶
Feature Importance & Permutation Importance
In [70]:
logisticRegressionModel.fit(X_train, y_train)
c:\Users\irina\ml-final-project\venv\Lib\site-packages\sklearn\linear_model\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Out[70]:
LogisticRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()
In [74]:
gradientBoostingModel.fit(X_train, y_train)
Out[74]:
GradientBoostingClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GradientBoostingClassifier()
In [75]:
randomForestModel.fit(X_train, y_train)
Out[75]:
RandomForestClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier()
In [76]:
decisionTreeModel.fit(X_train, y_train)
Out[76]:
DecisionTreeClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier()
In [77]:
nonLinearSupportVectorModel.fit(X_train, y_train)
Out[77]:
SVC()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SVC()
In [78]:
naiveBayesModel.fit(X_train, y_train)
Out[78]:
GaussianNB()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GaussianNB()
In [79]:
neuralNetworkModel.fit(X_train, y_train)
Epoch 1/2 294/294 ━━━━━━━━━━━━━━━━━━━━ 5s 7ms/step - accuracy: 0.2615 - loss: 12722.4248 Epoch 2/2 19/294 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.3571 - loss: 879.1167
c:\Users\irina\ml-final-project\venv\Lib\site-packages\keras\src\callbacks\early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
294/294 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.3308 - loss: 1311.2751
Out[79]:
FeedForwardNeuralNetwork(activations=['relu', 'relu', 'relu', 'relu'],
batch_size=128, epochs=2,
kernel_initializer='glorot_uniform',
learning_rate=0.001, n_layers=4, num_classes=12,
num_features=142, units_per_layer=[512, 256, 128, 64])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
FeedForwardNeuralNetwork(activations=['relu', 'relu', 'relu', 'relu'],
batch_size=128, epochs=2,
kernel_initializer='glorot_uniform',
learning_rate=0.001, n_layers=4, num_classes=12,
num_features=142, units_per_layer=[512, 256, 128, 64])In [105]:
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.inspection import permutation_importance
import pandas as pd
def plot_feature_importance_pipeline(model, X_train, y_train, model_name="Model"):
plt.figure(figsize=(10, 6))
if hasattr(model, 'feature_importances_'):
importances = model.feature_importances_
indices = importances.argsort()[::-1] # Descending order
sorted_features = [(X_train.columns[i], importances[i]) for i in indices]
plt.barh(range(len(indices)), importances[indices], align='center')
plt.yticks(range(len(indices)), [X_train.columns[i] for i in indices])
plt.gca().invert_yaxis() # Most important at top
plt.title(f'{model_name} - Feature Importance')
else:
result = permutation_importance(model, X_train, y_train, n_repeats=3, random_state=42)
sorted_idx = result.importances_mean.argsort()[::-1] # Descending order
sorted_features = [(X_train.columns[i], result.importances_mean[i]) for i in sorted_idx]
plt.barh(range(len(sorted_idx)), result.importances_mean[sorted_idx], align='center')
plt.yticks(range(len(sorted_idx)), [X_train.columns[i] for i in sorted_idx])
plt.gca().invert_yaxis() # Most important at top
plt.title(f'{model_name} - Permutation Importance')
plt.xlabel('Importance')
plt.tight_layout()
plt.show()
importance_df = pd.DataFrame(sorted_features, columns=['Feature', 'Importance'])
print("\nFeature importances:")
print(importance_df.to_string(index=False))
return importance_df
In [106]:
plot_feature_importance_pipeline(logisticRegressionModel, X_train, y_train, model_name="Logistic Regression")
Feature importances:
Feature Importance
Network_TotalTransmittedBytes 0.013130
Network_TotalReceivedBytes 0.005812
Memory_PrivateDirty 0.004350
Memory_PssTotal 0.002569
Memory_HeapSize 0.001807
Memory_HeapAlloc 0.001338
Logcat_total 0.000213
Logcat_error 0.000027
Logcat_verbose 0.000018
Memory_PssClean 0.000009
Logcat_info 0.000009
Battery_wakelock 0.000000
Reboot_before 0.000000
Battery_service 0.000000
API_IPC_android.content.ContextWrapper_sendBroadcast 0.000000
API_IPC_android.content.ContextWrapper_sendStickyBroadcast 0.000000
API_IPC_android.content.ContextWrapper_startActivity 0.000000
Network_TotalReceivedPackets 0.000000
Process_total 0.000000
API__sessions 0.000000
API_DeviceData_android.os.SystemProperties_get 0.000000
API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages 0.000000
API_DeviceData_android.accounts.AccountManager_getAccountsByType 0.000000
API_DeviceData_android.content.ContentResolver_delete 0.000000
API_DeviceData_android.content.ContentResolver_insert 0.000000
API_DeviceData_android.media.MediaRecorder_start 0.000000
API_DeviceData_android.media.AudioRecord_startRecording 0.000000
API_DeviceData_android.location.Location_getLongitude 0.000000
API_DeviceData_android.location.Location_getLatitude 0.000000
API_DeviceData_android.accounts.AccountManager_getAccounts 0.000000
API_SystemManager_android.telephony.TelephonyManager_listen 0.000000
API_SystemManager_android.app.NotificationManager_notify 0.000000
API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting 0.000000
API_Base64_android.util.Base64_encodeToString 0.000000
API_Base64_android.util.Base64_encode 0.000000
API_Base64_android.util.Base64_decode 0.000000
API_DexClassLoader_dalvik.system.DexClassLoader_$init 0.000000
API_DexClassLoader_dalvik.system.DexFile_loadClass 0.000000
API_DexClassLoader_dalvik.system.DexFile_loadDex 0.000000
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary 0.000000
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources 0.000000
API_DeviceData_android.content.ContentResolver_registerContentObserver 0.000000
API_DeviceData_android.content.ContentResolver_query 0.000000
API_SMS_android.telephony.SmsManager_sendMultipartTextMessage 0.000000
API_SMS_android.telephony.SmsManager_sendTextMessage 0.000000
API_SystemManager_android.content.BroadcastReceiver_abortBroadcast 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName 0.000000
API_DeviceInfo_android.os.Debug_isDebuggerConnected 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso 0.000000
API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId 0.000000
API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress 0.000000
API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID 0.000000
API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId 0.000000
API_Crypto-Hash_java.security.MessageDigest_update 0.000000
API_Crypto-Hash_java.security.MessageDigest_digest 0.000000
API_Crypto_javax.crypto.Cipher_doFinal 0.000000
API_Crypto_javax.crypto.spec.SecretKeySpec_$init 0.000000
API_Binder_android.app.Activity_startActivity 0.000000
API_Binder_android.app.ActivityThread_handleReceiver 0.000000
API_Binder_android.app.ContextImpl_registerReceiver 0.000000
API_IPC_android.content.ContextWrapper_registerReceiver 0.000000
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource 0.000000
API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream 0.000000
API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream 0.000000
API_Network_org.apache.http.impl.client.AbstractHttpClient_execute 0.000000
API_Network_java.net.URL_openConnection 0.000000
API_WebView_android.webkit.WebView_loadDataWithBaseURL 0.000000
API_WebView_android.webkit.WebView_addJavascriptInterface 0.000000
API_WebView_android.webkit.WebView_evaluateJavascript 0.000000
API_IPC_android.content.ContextWrapper_startService 0.000000
API_IPC_android.content.ContextWrapper_stopService 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_create 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_compileStatement 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_openDatabase 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_update 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_rawQuery 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_query 0.000000
API_Database_android.content.ContextWrapper_deleteDatabase 0.000000
API_Database_android.content.ContextWrapper_databaseList 0.000000
API_Database_android.content.ContextWrapper_openOrCreateDatabase 0.000000
API_FileIO_android.content.ContextWrapper_deleteFile 0.000000
API_FileIO_android.content.ContextWrapper_openFileOutput 0.000000
API_FileIO_android.content.ContextWrapper_openFileInput 0.000000
API_FileIO_libcore.io.IoBridge_open 0.000000
API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled 0.000000
API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword 0.000000
API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword 0.000000
API_WebView_android.webkit.WebView_savePassword 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_insert 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_getPath 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_execSQL 0.000000
Memory_LocalBinders 0.000000
Memory_ProxyBinders 0.000000
Memory_ParcelMemory 0.000000
API_WebView_android.webkit.WebView_postUrl 0.000000
API_WebView_android.webkit.WebView_postWebMessage 0.000000
API_WebView_android.webkit.WebView_loadData 0.000000
API_JavaNativeInterface_java.lang.Runtime_load 0.000000
API_WebView_android.webkit.WebView_loadUrl 0.000000
API_Process_android.os.Process_start 0.000000
Memory_WebViews 0.000000
Memory_OpenSSLSockets 0.000000
API_JavaNativeInterface_java.lang.Runtime_loadLibrary 0.000000
API_Command_java.lang.ProcessBuilder_start 0.000000
API_Command_java.lang.Runtime_exec 0.000000
API_Process_android.os.Process_killProcess 0.000000
API_Process_android.app.ActivityManager_killBackgroundProcesses 0.000000
Memory_SwapPssDirty 0.000000
Memory_Views 0.000000
Memory_ViewRootImpl 0.000000
Memory_ParcelCount 0.000000
Memory_DeathRecipients 0.000000
Memory_AssetManagers 0.000000
Memory_Activities 0.000000
Memory_Assets 0.000000
Memory_AppContexts 0.000000
Network_TotalTransmittedPackets -0.000009
Logcat_debug -0.000009
Logcat_warning -0.000009
Memory_HeapFree -0.000062
Memory_SharedDirty -0.000062
Memory_PrivateClean -0.000549
Memory_SharedClean -0.001701
Out[106]:
| Feature | Importance | |
|---|---|---|
| 0 | Network_TotalTransmittedBytes | 0.013130 |
| 1 | Network_TotalReceivedBytes | 0.005812 |
| 2 | Memory_PrivateDirty | 0.004350 |
| 3 | Memory_PssTotal | 0.002569 |
| 4 | Memory_HeapSize | 0.001807 |
| ... | ... | ... |
| 137 | Logcat_warning | -0.000009 |
| 138 | Memory_HeapFree | -0.000062 |
| 139 | Memory_SharedDirty | -0.000062 |
| 140 | Memory_PrivateClean | -0.000549 |
| 141 | Memory_SharedClean | -0.001701 |
142 rows × 2 columns
In [81]:
plot_feature_importance_pipeline(gradientBoostingModel, X_train, y_train, model_name="Gradient Boosting")
Feature importances:
Feature Importance
API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress 0.074970
API_IPC_android.content.ContextWrapper_stopService 0.060140
API_DeviceData_android.content.ContentResolver_registerContentObserver 0.044347
API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId 0.044082
API_FileIO_android.content.ContextWrapper_deleteFile 0.032765
API_SystemManager_android.content.BroadcastReceiver_abortBroadcast 0.025771
API_Command_java.lang.ProcessBuilder_start 0.024228
API_IPC_android.content.ContextWrapper_startActivity 0.023259
Memory_PrivateClean 0.022917
API_Network_java.net.URL_openConnection 0.021839
API_WebView_android.webkit.WebView_addJavascriptInterface 0.021818
API_Base64_android.util.Base64_decode 0.021282
API_DexClassLoader_dalvik.system.DexClassLoader_$init 0.021052
Memory_PrivateDirty 0.020860
API_DeviceData_android.os.SystemProperties_get 0.019572
API_Binder_android.app.ActivityThread_handleReceiver 0.018148
API_FileIO_android.content.ContextWrapper_openFileOutput 0.017400
API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber 0.017129
API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting 0.016273
API_Crypto-Hash_java.security.MessageDigest_update 0.015409
API_DeviceInfo_android.os.Debug_isDebuggerConnected 0.015370
API_Crypto-Hash_java.security.MessageDigest_digest 0.015146
API_SystemManager_android.telephony.TelephonyManager_listen 0.012592
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId 0.012255
Network_TotalTransmittedPackets 0.011821
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName 0.011800
API_IPC_android.content.ContextWrapper_startService 0.011548
Memory_Views 0.011518
API_Command_java.lang.Runtime_exec 0.011340
API_Binder_android.app.Activity_startActivity 0.011309
API_SMS_android.telephony.SmsManager_sendTextMessage 0.010995
API_IPC_android.content.ContextWrapper_sendBroadcast 0.010365
API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName 0.010202
Memory_SharedDirty 0.009824
API_IPC_android.content.ContextWrapper_registerReceiver 0.009793
API_Database_android.content.ContextWrapper_databaseList 0.009566
API_WebView_android.webkit.WebView_loadUrl 0.009347
Memory_SharedClean 0.008251
API_Process_android.app.ActivityManager_killBackgroundProcesses 0.008188
Memory_ViewRootImpl 0.007991
Memory_HeapSize 0.007823
Memory_ParcelMemory 0.007818
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources 0.007706
API_Binder_android.app.ContextImpl_registerReceiver 0.007600
Network_TotalTransmittedBytes 0.007221
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion 0.007113
API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number 0.006738
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary 0.006663
API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages 0.006173
Network_TotalReceivedBytes 0.006010
Memory_HeapAlloc 0.005711
API__sessions 0.005491
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator 0.005490
API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream 0.005440
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource 0.004979
API_Database_android.database.sqlite.SQLiteDatabase_update 0.004967
Memory_PssTotal 0.004846
API_Database_android.database.sqlite.SQLiteDatabase_openDatabase 0.004822
Memory_AppContexts 0.004581
Battery_service 0.004515
API_Crypto_javax.crypto.Cipher_doFinal 0.004468
Memory_ParcelCount 0.004431
API_DeviceData_android.content.ContentResolver_query 0.004344
Memory_HeapFree 0.004037
API_Database_android.database.sqlite.SQLiteDatabase_rawQuery 0.003894
Network_TotalReceivedPackets 0.003719
Memory_LocalBinders 0.003698
API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow 0.003610
API_DexClassLoader_dalvik.system.DexFile_loadClass 0.003526
Battery_wakelock 0.003367
API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso 0.003307
API_Crypto_javax.crypto.spec.SecretKeySpec_$init 0.003276
Memory_Activities 0.003054
API_DexClassLoader_dalvik.system.DexFile_loadDex 0.002916
API_FileIO_android.content.ContextWrapper_openFileInput 0.002862
API_Base64_android.util.Base64_encode 0.002392
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso 0.002364
Memory_WebViews 0.002329
API_FileIO_libcore.io.IoBridge_open 0.002288
Logcat_total 0.002187
API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase 0.001865
API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory 0.001852
Memory_PssClean 0.001700
API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory 0.001569
API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId 0.001543
API_SystemManager_android.app.NotificationManager_notify 0.001455
API_SMS_android.telephony.SmsManager_sendMultipartTextMessage 0.001358
Memory_ProxyBinders 0.001332
API_DeviceData_android.accounts.AccountManager_getAccountsByType 0.001082
API_WebView_android.webkit.WebView_loadData 0.001050
API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID 0.001046
API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress 0.000955
Memory_Assets 0.000919
API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict 0.000919
API_DeviceData_android.accounts.AccountManager_getAccounts 0.000894
API_Database_android.database.sqlite.SQLiteDatabase_execSQL 0.000889
API_Base64_android.util.Base64_encodeToString 0.000860
API_WebView_android.webkit.WebView_loadDataWithBaseURL 0.000791
Memory_OpenSSLSockets 0.000689
Logcat_info 0.000674
API_Database_android.database.sqlite.SQLiteDatabase_compileStatement 0.000665
API_Database_android.database.sqlite.SQLiteDatabase_query 0.000650
Logcat_error 0.000591
Memory_DeathRecipients 0.000582
API_Database_android.database.sqlite.SQLiteDatabase_getPath 0.000526
Logcat_warning 0.000417
API_Database_android.content.ContextWrapper_openOrCreateDatabase 0.000398
Process_total 0.000396
Logcat_verbose 0.000356
Logcat_debug 0.000319
API_Database_android.database.sqlite.SQLiteDatabase_insert 0.000315
API_DeviceData_android.content.ContentResolver_insert 0.000293
API_WebView_android.webkit.WebView_evaluateJavascript 0.000209
API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict 0.000144
API_DeviceData_android.content.ContentResolver_delete 0.000115
API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled 0.000079
API_WebView_android.webkit.WebView_postUrl 0.000073
API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase 0.000060
API_Process_android.os.Process_killProcess 0.000050
API_DeviceData_android.media.AudioRecord_startRecording 0.000032
API_Database_android.content.ContextWrapper_deleteDatabase 0.000019
API_DeviceData_android.media.MediaRecorder_start 0.000008
Reboot_before 0.000001
API_DeviceData_android.location.Location_getLongitude 0.000000
API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream 0.000000
API_Network_org.apache.http.impl.client.AbstractHttpClient_execute 0.000000
API_IPC_android.content.ContextWrapper_sendStickyBroadcast 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages 0.000000
API_DeviceData_android.location.Location_getLatitude 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_create 0.000000
API_WebView_android.webkit.WebView_postWebMessage 0.000000
API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword 0.000000
API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword 0.000000
API_WebView_android.webkit.WebView_savePassword 0.000000
API_JavaNativeInterface_java.lang.Runtime_loadLibrary 0.000000
API_Process_android.os.Process_start 0.000000
API_JavaNativeInterface_java.lang.Runtime_load 0.000000
Memory_AssetManagers 0.000000
Memory_SwapPssDirty 0.000000
Out[81]:
| Feature | Importance | |
|---|---|---|
| 0 | API_DeviceInfo_android.net.wifi.WifiInfo_getMa... | 0.074970 |
| 1 | API_IPC_android.content.ContextWrapper_stopSer... | 0.060140 |
| 2 | API_DeviceData_android.content.ContentResolver... | 0.044347 |
| 3 | API_DeviceInfo_android.telephony.TelephonyMana... | 0.044082 |
| 4 | API_FileIO_android.content.ContextWrapper_dele... | 0.032765 |
| ... | ... | ... |
| 137 | API_JavaNativeInterface_java.lang.Runtime_load... | 0.000000 |
| 138 | API_Process_android.os.Process_start | 0.000000 |
| 139 | API_JavaNativeInterface_java.lang.Runtime_load | 0.000000 |
| 140 | Memory_AssetManagers | 0.000000 |
| 141 | Memory_SwapPssDirty | 0.000000 |
142 rows × 2 columns
In [82]:
plot_feature_importance_pipeline(randomForestModel, X_train, y_train, model_name="Random Forest")
Feature importances:
Feature Importance
API_Crypto-Hash_java.security.MessageDigest_digest 0.021725
Memory_HeapAlloc 0.021683
Memory_HeapSize 0.021184
Memory_PrivateDirty 0.021131
Memory_SharedDirty 0.020819
Memory_PrivateClean 0.019716
API_DeviceData_android.os.SystemProperties_get 0.019459
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId 0.019340
Memory_PssClean 0.018371
API_Crypto-Hash_java.security.MessageDigest_update 0.018248
API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress 0.017983
API_Binder_android.app.Activity_startActivity 0.017402
Memory_SharedClean 0.017384
API_DeviceData_android.content.ContentResolver_registerContentObserver 0.017305
API_IPC_android.content.ContextWrapper_startService 0.017090
API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId 0.016932
API_Network_java.net.URL_openConnection 0.016682
Memory_HeapFree 0.016590
Network_TotalTransmittedBytes 0.016510
Memory_LocalBinders 0.016322
Memory_Views 0.016261
API_Command_java.lang.Runtime_exec 0.016242
Memory_ParcelMemory 0.015622
Memory_PssTotal 0.015497
Network_TotalTransmittedPackets 0.015421
API_Binder_android.app.ActivityThread_handleReceiver 0.015193
API_IPC_android.content.ContextWrapper_registerReceiver 0.015000
API_Binder_android.app.ContextImpl_registerReceiver 0.014464
API_Command_java.lang.ProcessBuilder_start 0.014375
Memory_ParcelCount 0.013795
API__sessions 0.013308
Network_TotalReceivedBytes 0.012097
API_FileIO_android.content.ContextWrapper_deleteFile 0.011413
Logcat_total 0.011408
Memory_ProxyBinders 0.011321
Memory_AppContexts 0.011022
Network_TotalReceivedPackets 0.010685
API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream 0.010350
API_WebView_android.webkit.WebView_loadUrl 0.010320
Battery_service 0.009997
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary 0.009945
API_DexClassLoader_dalvik.system.DexClassLoader_$init 0.009907
Battery_wakelock 0.009599
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource 0.009353
API_Base64_android.util.Base64_decode 0.009327
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName 0.009306
Memory_ViewRootImpl 0.009283
API_DeviceData_android.content.ContentResolver_query 0.009069
Logcat_verbose 0.008686
Logcat_error 0.008683
Logcat_debug 0.008605
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso 0.008546
Logcat_warning 0.008409
API_Crypto_javax.crypto.Cipher_doFinal 0.008356
Logcat_info 0.008267
API_Crypto_javax.crypto.spec.SecretKeySpec_$init 0.007742
API_FileIO_libcore.io.IoBridge_open 0.007422
API_FileIO_android.content.ContextWrapper_openFileOutput 0.007290
API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages 0.007230
API_WebView_android.webkit.WebView_addJavascriptInterface 0.007199
API_Database_android.database.sqlite.SQLiteDatabase_getPath 0.007166
API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber 0.007094
API_DeviceData_android.content.ContentResolver_delete 0.006897
API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting 0.006780
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator 0.006770
API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory 0.006700
Memory_Activities 0.006330
API_IPC_android.content.ContextWrapper_sendBroadcast 0.006315
API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict 0.006280
API_Database_android.database.sqlite.SQLiteDatabase_openDatabase 0.006247
Memory_WebViews 0.006197
Process_total 0.006093
API_IPC_android.content.ContextWrapper_startActivity 0.006008
API_Database_android.database.sqlite.SQLiteDatabase_compileStatement 0.005771
API_Base64_android.util.Base64_encode 0.005730
API_Base64_android.util.Base64_encodeToString 0.005422
API_Database_android.database.sqlite.SQLiteDatabase_update 0.005419
API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number 0.005327
API_Database_android.database.sqlite.SQLiteDatabase_rawQuery 0.004973
API_FileIO_android.content.ContextWrapper_openFileInput 0.004348
Memory_DeathRecipients 0.004163
API_DeviceData_android.content.ContentResolver_insert 0.004001
Memory_Assets 0.003958
API_SystemManager_android.telephony.TelephonyManager_listen 0.003825
API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory 0.003702
API_SMS_android.telephony.SmsManager_sendTextMessage 0.003697
API_DeviceInfo_android.os.Debug_isDebuggerConnected 0.003576
API_SystemManager_android.content.BroadcastReceiver_abortBroadcast 0.003440
API_IPC_android.content.ContextWrapper_stopService 0.003405
API_Database_android.database.sqlite.SQLiteDatabase_query 0.003396
API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName 0.002915
API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso 0.002749
API_Database_android.database.sqlite.SQLiteDatabase_execSQL 0.002625
API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict 0.002482
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources 0.002119
API_WebView_android.webkit.WebView_loadDataWithBaseURL 0.002057
API_DexClassLoader_dalvik.system.DexFile_loadDex 0.001925
Reboot_before 0.001613
API_Database_android.database.sqlite.SQLiteDatabase_insert 0.001561
API_SMS_android.telephony.SmsManager_sendMultipartTextMessage 0.001542
Memory_OpenSSLSockets 0.001272
API_DexClassLoader_dalvik.system.DexFile_loadClass 0.001272
API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress 0.001152
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion 0.001061
API_Database_android.content.ContextWrapper_databaseList 0.001025
API_Process_android.app.ActivityManager_killBackgroundProcesses 0.000949
API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID 0.000944
API_SystemManager_android.app.NotificationManager_notify 0.000613
API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow 0.000560
API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase 0.000542
API_DeviceData_android.accounts.AccountManager_getAccounts 0.000499
API_DeviceData_android.accounts.AccountManager_getAccountsByType 0.000385
API_Database_android.content.ContextWrapper_openOrCreateDatabase 0.000359
API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId 0.000283
API_WebView_android.webkit.WebView_loadData 0.000240
API_WebView_android.webkit.WebView_evaluateJavascript 0.000177
API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled 0.000038
API_DeviceData_android.location.Location_getLongitude 0.000029
API_DeviceData_android.location.Location_getLatitude 0.000022
API_DeviceData_android.media.MediaRecorder_start 0.000018
API_DeviceData_android.media.AudioRecord_startRecording 0.000016
API_Database_android.content.ContextWrapper_deleteDatabase 0.000012
API_WebView_android.webkit.WebView_postUrl 0.000009
API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase 0.000007
API_Process_android.os.Process_killProcess 0.000007
API_IPC_android.content.ContextWrapper_sendStickyBroadcast 0.000002
API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName 0.000000
API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream 0.000000
API_Network_org.apache.http.impl.client.AbstractHttpClient_execute 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_create 0.000000
API_WebView_android.webkit.WebView_postWebMessage 0.000000
API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword 0.000000
API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword 0.000000
API_WebView_android.webkit.WebView_savePassword 0.000000
API_JavaNativeInterface_java.lang.Runtime_loadLibrary 0.000000
API_Process_android.os.Process_start 0.000000
API_JavaNativeInterface_java.lang.Runtime_load 0.000000
Memory_AssetManagers 0.000000
Memory_SwapPssDirty 0.000000
Out[82]:
| Feature | Importance | |
|---|---|---|
| 0 | API_Crypto-Hash_java.security.MessageDigest_di... | 0.021725 |
| 1 | Memory_HeapAlloc | 0.021683 |
| 2 | Memory_HeapSize | 0.021184 |
| 3 | Memory_PrivateDirty | 0.021131 |
| 4 | Memory_SharedDirty | 0.020819 |
| ... | ... | ... |
| 137 | API_JavaNativeInterface_java.lang.Runtime_load... | 0.000000 |
| 138 | API_Process_android.os.Process_start | 0.000000 |
| 139 | API_JavaNativeInterface_java.lang.Runtime_load | 0.000000 |
| 140 | Memory_AssetManagers | 0.000000 |
| 141 | Memory_SwapPssDirty | 0.000000 |
142 rows × 2 columns
In [83]:
plot_feature_importance_pipeline(decisionTreeModel, X_train, y_train, model_name="Decision Tree")
Feature importances:
Feature Importance
API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress 0.069452
API_DeviceData_android.content.ContentResolver_registerContentObserver 0.069172
API_DeviceData_android.content.ContentResolver_insert 0.040054
API_Network_java.net.URL_openConnection 0.039348
Memory_PrivateDirty 0.034660
API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages 0.028236
API_Command_java.lang.Runtime_exec 0.024447
API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting 0.023315
API_Binder_android.app.ActivityThread_handleReceiver 0.022200
API_Binder_android.app.Activity_startActivity 0.021996
API_IPC_android.content.ContextWrapper_startActivity 0.018793
API_DeviceData_android.os.SystemProperties_get 0.018496
Memory_HeapAlloc 0.017029
API_SystemManager_android.content.BroadcastReceiver_abortBroadcast 0.016002
API_IPC_android.content.ContextWrapper_startService 0.015893
Memory_SharedClean 0.014930
API_Base64_android.util.Base64_decode 0.014272
API_Binder_android.app.ContextImpl_registerReceiver 0.013909
API_SystemManager_android.telephony.TelephonyManager_listen 0.013648
Memory_HeapSize 0.013261
API_Crypto-Hash_java.security.MessageDigest_digest 0.013167
API_Crypto-Hash_java.security.MessageDigest_update 0.012598
Memory_SharedDirty 0.012438
Memory_Views 0.012264
API__sessions 0.012125
API_FileIO_android.content.ContextWrapper_openFileOutput 0.011485
API_DexClassLoader_dalvik.system.DexClassLoader_$init 0.011471
Memory_HeapFree 0.011043
API_SMS_android.telephony.SmsManager_sendTextMessage 0.010661
API_DeviceData_android.content.ContentResolver_query 0.010493
Memory_ViewRootImpl 0.010422
Memory_PssTotal 0.010260
Logcat_total 0.010075
Memory_ParcelCount 0.009613
Memory_PrivateClean 0.009426
Logcat_error 0.009018
Memory_PssClean 0.008710
API_WebView_android.webkit.WebView_addJavascriptInterface 0.008709
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary 0.008309
Memory_ParcelMemory 0.008229
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator 0.008148
API_Crypto_javax.crypto.spec.SecretKeySpec_$init 0.008068
API_IPC_android.content.ContextWrapper_registerReceiver 0.008060
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso 0.007857
Logcat_info 0.007832
Logcat_verbose 0.007708
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources 0.007662
Network_TotalTransmittedBytes 0.007600
Memory_DeathRecipients 0.007512
Logcat_warning 0.007463
API_DeviceInfo_android.os.Debug_isDebuggerConnected 0.007390
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource 0.007052
Logcat_debug 0.006657
API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId 0.006649
Memory_AppContexts 0.006494
Memory_ProxyBinders 0.006439
API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream 0.006192
API_IPC_android.content.ContextWrapper_sendBroadcast 0.006137
Network_TotalReceivedBytes 0.005902
Memory_LocalBinders 0.005765
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId 0.005726
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName 0.005669
API_FileIO_android.content.ContextWrapper_deleteFile 0.005448
API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number 0.005337
Network_TotalTransmittedPackets 0.005173
API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber 0.005105
Battery_service 0.005047
Process_total 0.004490
API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName 0.003883
API_DexClassLoader_dalvik.system.DexFile_loadDex 0.003718
API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso 0.003493
API_Database_android.database.sqlite.SQLiteDatabase_openDatabase 0.003423
API_IPC_android.content.ContextWrapper_stopService 0.003378
Battery_wakelock 0.003244
API_FileIO_android.content.ContextWrapper_openFileInput 0.003065
API_Base64_android.util.Base64_encodeToString 0.003002
Memory_Activities 0.002859
API_Crypto_javax.crypto.Cipher_doFinal 0.002843
API_Database_android.content.ContextWrapper_databaseList 0.002609
Memory_Assets 0.002532
Network_TotalReceivedPackets 0.002522
API_FileIO_libcore.io.IoBridge_open 0.002403
API_WebView_android.webkit.WebView_loadUrl 0.002320
API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress 0.002244
API_Database_android.database.sqlite.SQLiteDatabase_rawQuery 0.002094
API_Database_android.database.sqlite.SQLiteDatabase_query 0.002071
API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory 0.002049
API_Database_android.database.sqlite.SQLiteDatabase_getPath 0.001957
Memory_WebViews 0.001955
API_Database_android.database.sqlite.SQLiteDatabase_insert 0.001926
API_Database_android.database.sqlite.SQLiteDatabase_compileStatement 0.001722
API_Database_android.database.sqlite.SQLiteDatabase_execSQL 0.001690
API_Command_java.lang.ProcessBuilder_start 0.001435
API_Base64_android.util.Base64_encode 0.001388
API_WebView_android.webkit.WebView_loadDataWithBaseURL 0.001286
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion 0.001273
API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory 0.001048
API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID 0.000838
API_SystemManager_android.app.NotificationManager_notify 0.000724
Reboot_before 0.000654
API_Database_android.database.sqlite.SQLiteDatabase_update 0.000647
Memory_OpenSSLSockets 0.000571
API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict 0.000426
API_DeviceData_android.accounts.AccountManager_getAccounts 0.000389
API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict 0.000361
API_DeviceData_android.accounts.AccountManager_getAccountsByType 0.000288
API_Database_android.content.ContextWrapper_openOrCreateDatabase 0.000259
API_WebView_android.webkit.WebView_loadData 0.000222
API_WebView_android.webkit.WebView_evaluateJavascript 0.000195
API_DeviceData_android.media.MediaRecorder_start 0.000168
API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled 0.000111
API_DeviceData_android.content.ContentResolver_delete 0.000110
API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase 0.000087
API_DexClassLoader_dalvik.system.DexFile_loadClass 0.000083
API_Process_android.os.Process_killProcess 0.000065
API_Process_android.app.ActivityManager_killBackgroundProcesses 0.000065
API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow 0.000061
API_SMS_android.telephony.SmsManager_sendMultipartTextMessage 0.000033
API_DeviceData_android.location.Location_getLongitude 0.000000
API_DeviceData_android.media.AudioRecord_startRecording 0.000000
API_DeviceData_android.location.Location_getLatitude 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages 0.000000
API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId 0.000000
API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream 0.000000
API_Network_org.apache.http.impl.client.AbstractHttpClient_execute 0.000000
API_IPC_android.content.ContextWrapper_sendStickyBroadcast 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications 0.000000
API_WebView_android.webkit.WebView_postWebMessage 0.000000
API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword 0.000000
API_Database_android.content.ContextWrapper_deleteDatabase 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_create 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase 0.000000
API_WebView_android.webkit.WebView_postUrl 0.000000
API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword 0.000000
API_WebView_android.webkit.WebView_savePassword 0.000000
API_JavaNativeInterface_java.lang.Runtime_loadLibrary 0.000000
API_Process_android.os.Process_start 0.000000
API_JavaNativeInterface_java.lang.Runtime_load 0.000000
Memory_AssetManagers 0.000000
Memory_SwapPssDirty 0.000000
Out[83]:
| Feature | Importance | |
|---|---|---|
| 0 | API_DeviceInfo_android.net.wifi.WifiInfo_getMa... | 0.069452 |
| 1 | API_DeviceData_android.content.ContentResolver... | 0.069172 |
| 2 | API_DeviceData_android.content.ContentResolver... | 0.040054 |
| 3 | API_Network_java.net.URL_openConnection | 0.039348 |
| 4 | Memory_PrivateDirty | 0.034660 |
| ... | ... | ... |
| 137 | API_JavaNativeInterface_java.lang.Runtime_load... | 0.000000 |
| 138 | API_Process_android.os.Process_start | 0.000000 |
| 139 | API_JavaNativeInterface_java.lang.Runtime_load | 0.000000 |
| 140 | Memory_AssetManagers | 0.000000 |
| 141 | Memory_SwapPssDirty | 0.000000 |
142 rows × 2 columns
In [84]:
plot_feature_importance_pipeline(naiveBayesModel, X_train, y_train, model_name="Naive Bayes")
Feature importances:
Feature Importance
API_Base64_android.util.Base64_decode 0.044589
Memory_PssClean 0.006822
Memory_SharedClean 0.006538
API_Crypto-Hash_java.security.MessageDigest_update 0.005723
Memory_PrivateClean 0.004208
Memory_SharedDirty 0.002720
Memory_PrivateDirty 0.002365
API_Crypto-Hash_java.security.MessageDigest_digest 0.001940
Network_TotalTransmittedBytes 0.001931
Memory_HeapFree 0.001825
Logcat_info 0.000629
API_DeviceData_android.os.SystemProperties_get 0.000416
API_SystemManager_android.app.NotificationManager_notify 0.000354
Memory_PssTotal 0.000346
API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number 0.000283
Memory_Views 0.000106
API_Crypto_javax.crypto.Cipher_doFinal 0.000106
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId 0.000089
API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict 0.000044
API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress 0.000027
API_Database_android.database.sqlite.SQLiteDatabase_execSQL 0.000027
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource 0.000027
API_DexClassLoader_dalvik.system.DexFile_loadClass 0.000018
API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict 0.000018
API_IPC_android.content.ContextWrapper_startActivity 0.000018
API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting 0.000009
Process_total 0.000000
Reboot_before 0.000000
Battery_wakelock 0.000000
Battery_service 0.000000
Memory_Assets 0.000000
Memory_ViewRootImpl 0.000000
Memory_ProxyBinders 0.000000
Memory_LocalBinders 0.000000
Memory_DeathRecipients 0.000000
API_WebView_android.webkit.WebView_loadDataWithBaseURL 0.000000
API_WebView_android.webkit.WebView_evaluateJavascript 0.000000
API_WebView_android.webkit.WebView_addJavascriptInterface 0.000000
API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream 0.000000
API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream 0.000000
API_SMS_android.telephony.SmsManager_sendTextMessage 0.000000
API_SystemManager_android.content.BroadcastReceiver_abortBroadcast 0.000000
API_SMS_android.telephony.SmsManager_sendMultipartTextMessage 0.000000
API_DeviceData_android.accounts.AccountManager_getAccountsByType 0.000000
API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages 0.000000
API__sessions 0.000000
API_IPC_android.content.ContextWrapper_sendBroadcast 0.000000
API_IPC_android.content.ContextWrapper_stopService 0.000000
API_IPC_android.content.ContextWrapper_sendStickyBroadcast 0.000000
API_Binder_android.app.Activity_startActivity 0.000000
API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId 0.000000
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary 0.000000
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules 0.000000
API_DeviceInfo_android.os.Debug_isDebuggerConnected 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion 0.000000
API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber 0.000000
API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName 0.000000
API_Binder_android.app.ActivityThread_handleReceiver 0.000000
API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_openDatabase 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_create 0.000000
API_DexClassLoader_dalvik.system.DexClassLoader_$init 0.000000
API_DexClassLoader_dalvik.system.DexFile_loadDex 0.000000
API_SystemManager_android.telephony.TelephonyManager_listen 0.000000
API_Network_org.apache.http.impl.client.AbstractHttpClient_execute 0.000000
API_DeviceData_android.content.ContentResolver_delete 0.000000
API_DeviceData_android.content.ContentResolver_insert 0.000000
API_DeviceData_android.media.MediaRecorder_start 0.000000
API_DeviceData_android.media.AudioRecord_startRecording 0.000000
API_DeviceData_android.location.Location_getLongitude 0.000000
API_DeviceData_android.location.Location_getLatitude 0.000000
API_DeviceData_android.accounts.AccountManager_getAccounts 0.000000
Memory_SwapPssDirty 0.000000
Memory_AssetManagers 0.000000
Memory_Activities 0.000000
Memory_AppContexts 0.000000
Memory_WebViews 0.000000
Memory_OpenSSLSockets 0.000000
Memory_ParcelMemory 0.000000
API_Process_android.os.Process_start 0.000000
API_WebView_android.webkit.WebView_loadData 0.000000
API_WebView_android.webkit.WebView_loadUrl 0.000000
API_JavaNativeInterface_java.lang.Runtime_load 0.000000
API_JavaNativeInterface_java.lang.Runtime_loadLibrary 0.000000
API_Command_java.lang.ProcessBuilder_start 0.000000
API_Process_android.os.Process_killProcess 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_compileStatement 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_update 0.000000
API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword 0.000000
API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword 0.000000
API_WebView_android.webkit.WebView_savePassword 0.000000
API_WebView_android.webkit.WebView_postWebMessage 0.000000
API_WebView_android.webkit.WebView_postUrl 0.000000
API_FileIO_android.content.ContextWrapper_openFileInput 0.000000
API_FileIO_libcore.io.IoBridge_open 0.000000
API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_insert 0.000000
API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase 0.000000
API_FileIO_android.content.ContextWrapper_openFileOutput 0.000000
API_FileIO_android.content.ContextWrapper_deleteFile 0.000000
API_Database_android.content.ContextWrapper_openOrCreateDatabase 0.000000
API_Database_android.content.ContextWrapper_databaseList 0.000000
API_Database_android.content.ContextWrapper_deleteDatabase 0.000000
API_Process_android.app.ActivityManager_killBackgroundProcesses 0.000000
API_Command_java.lang.Runtime_exec -0.000009
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName -0.000009
API_Database_android.database.sqlite.SQLiteDatabase_rawQuery -0.000009
API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId -0.000009
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator -0.000009
API_Network_java.net.URL_openConnection -0.000009
API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID -0.000009
Memory_ParcelCount -0.000009
API_Base64_android.util.Base64_encodeToString -0.000009
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso -0.000009
API_DeviceData_android.content.ContentResolver_query -0.000009
API_Base64_android.util.Base64_encode -0.000009
API_IPC_android.content.ContextWrapper_startService -0.000027
API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory -0.000027
API_Database_android.database.sqlite.SQLiteDatabase_query -0.000027
API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory -0.000035
API_IPC_android.content.ContextWrapper_registerReceiver -0.000035
API_Binder_android.app.ContextImpl_registerReceiver -0.000044
API_Database_android.database.sqlite.SQLiteDatabase_getPath -0.000053
Logcat_debug -0.000097
API_DeviceData_android.content.ContentResolver_registerContentObserver -0.000106
Logcat_verbose -0.000133
API_Crypto_javax.crypto.spec.SecretKeySpec_$init -0.000142
Logcat_error -0.000230
Memory_HeapSize -0.000275
Logcat_warning -0.000301
Memory_HeapAlloc -0.000381
Network_TotalReceivedBytes -0.000532
Logcat_total -0.000664
Network_TotalReceivedPackets -0.001169
Network_TotalTransmittedPackets -0.001311
Out[84]:
| Feature | Importance | |
|---|---|---|
| 0 | API_Base64_android.util.Base64_decode | 0.044589 |
| 1 | Memory_PssClean | 0.006822 |
| 2 | Memory_SharedClean | 0.006538 |
| 3 | API_Crypto-Hash_java.security.MessageDigest_up... | 0.005723 |
| 4 | Memory_PrivateClean | 0.004208 |
| ... | ... | ... |
| 137 | Memory_HeapAlloc | -0.000381 |
| 138 | Network_TotalReceivedBytes | -0.000532 |
| 139 | Logcat_total | -0.000664 |
| 140 | Network_TotalReceivedPackets | -0.001169 |
| 141 | Network_TotalTransmittedPackets | -0.001311 |
142 rows × 2 columns
In [85]:
plot_feature_importance_pipeline(neuralNetworkModel, X_train, y_train, model_name="Neural Network")
1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step 1176/1176 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━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step
Feature importances:
Feature Importance
API_Crypto-Hash_java.security.MessageDigest_update 7.707641e-04
API_Crypto-Hash_java.security.MessageDigest_digest 6.467331e-04
Logcat_error 2.569214e-04
API_Database_android.database.sqlite.SQLiteDatabase_rawQuery 2.126246e-04
Memory_SharedDirty 1.771872e-04
API_Crypto_javax.crypto.Cipher_doFinal 1.594684e-04
Network_TotalReceivedBytes 1.506091e-04
Logcat_verbose 1.417497e-04
Memory_Views 1.417497e-04
API_Crypto_javax.crypto.spec.SecretKeySpec_$init 1.328904e-04
Memory_HeapFree 1.063123e-04
API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId 9.745293e-05
API_Base64_android.util.Base64_encode 9.745293e-05
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName 9.745293e-05
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary 7.973422e-05
API_Base64_android.util.Base64_encodeToString 7.973422e-05
API_IPC_android.content.ContextWrapper_registerReceiver 7.087486e-05
API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream 6.201550e-05
API_Base64_android.util.Base64_decode 5.315615e-05
API_DeviceData_android.os.SystemProperties_get 4.429679e-05
API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber 4.429679e-05
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso 3.543743e-05
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId 3.543743e-05
API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress 2.657807e-05
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource 2.657807e-05
API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number 2.657807e-05
API_DexClassLoader_dalvik.system.DexClassLoader_$init 2.657807e-05
Logcat_info 2.657807e-05
API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory 1.771872e-05
API_Database_android.database.sqlite.SQLiteDatabase_getPath 1.771872e-05
API_IPC_android.content.ContextWrapper_startService 8.859358e-06
API_Process_android.app.ActivityManager_killBackgroundProcesses 8.859358e-06
API_FileIO_android.content.ContextWrapper_openFileOutput 8.859358e-06
API_WebView_android.webkit.WebView_addJavascriptInterface 8.859358e-06
API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages 8.859358e-06
API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting 8.859358e-06
API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator 8.859358e-06
API_Binder_android.app.ContextImpl_registerReceiver 8.859358e-06
Battery_service 8.859358e-06
Logcat_debug 1.156482e-18
Memory_ParcelCount 1.156482e-18
API_DeviceData_android.content.ContentResolver_registerContentObserver 1.156482e-18
API_Command_java.lang.Runtime_exec 1.156482e-18
API_Command_java.lang.ProcessBuilder_start 1.156482e-18
Logcat_warning 1.156482e-18
Battery_wakelock 0.000000e+00
Memory_SwapPssDirty 0.000000e+00
Memory_Assets 0.000000e+00
Memory_DeathRecipients 0.000000e+00
Memory_WebViews 0.000000e+00
API_Binder_android.app.ActivityThread_handleReceiver 0.000000e+00
Reboot_before 0.000000e+00
API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream 0.000000e+00
API_Network_org.apache.http.impl.client.AbstractHttpClient_execute 0.000000e+00
API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName 0.000000e+00
API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId 0.000000e+00
API_DeviceData_android.content.ContentResolver_delete 0.000000e+00
API_DeviceData_android.content.ContentResolver_insert 0.000000e+00
API_SMS_android.telephony.SmsManager_sendMultipartTextMessage 0.000000e+00
API_SMS_android.telephony.SmsManager_sendTextMessage 0.000000e+00
API_SystemManager_android.content.BroadcastReceiver_abortBroadcast 0.000000e+00
API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName 0.000000e+00
API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules 0.000000e+00
API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages 0.000000e+00
API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress 0.000000e+00
API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID 0.000000e+00
API_DexClassLoader_dalvik.system.DexFile_loadDex 0.000000e+00
API_SystemManager_android.app.NotificationManager_notify 0.000000e+00
API_SystemManager_android.telephony.TelephonyManager_listen 0.000000e+00
API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources 0.000000e+00
API_DeviceData_android.accounts.AccountManager_getAccounts 0.000000e+00
API_DeviceData_android.accounts.AccountManager_getAccountsByType 0.000000e+00
API_DexClassLoader_dalvik.system.DexFile_loadClass 0.000000e+00
API__sessions 0.000000e+00
API_DeviceData_android.media.AudioRecord_startRecording 0.000000e+00
API_DeviceData_android.location.Location_getLongitude 0.000000e+00
API_DeviceData_android.location.Location_getLatitude 0.000000e+00
API_DeviceData_android.media.MediaRecorder_start 0.000000e+00
API_Process_android.os.Process_killProcess 0.000000e+00
API_Process_android.os.Process_start 0.000000e+00
API_WebView_android.webkit.WebView_loadData 0.000000e+00
API_JavaNativeInterface_java.lang.Runtime_loadLibrary 0.000000e+00
Memory_AssetManagers 0.000000e+00
API_IPC_android.content.ContextWrapper_stopService 0.000000e+00
API_IPC_android.content.ContextWrapper_startActivity 0.000000e+00
API_IPC_android.content.ContextWrapper_sendStickyBroadcast 0.000000e+00
API_DeviceInfo_android.os.Debug_isDebuggerConnected 0.000000e+00
API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion 0.000000e+00
API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications 0.000000e+00
API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso 0.000000e+00
API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow 0.000000e+00
API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase 0.000000e+00
API_Database_android.database.sqlite.SQLiteDatabase_update 0.000000e+00
API_Database_android.database.sqlite.SQLiteDatabase_create 0.000000e+00
API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled 0.000000e+00
API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword 0.000000e+00
API_WebView_android.webkit.WebView_savePassword 0.000000e+00
API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword 0.000000e+00
API_WebView_android.webkit.WebView_postWebMessage 0.000000e+00
API_WebView_android.webkit.WebView_postUrl 0.000000e+00
API_WebView_android.webkit.WebView_evaluateJavascript 0.000000e+00
API_Database_android.content.ContextWrapper_databaseList 0.000000e+00
API_WebView_android.webkit.WebView_loadDataWithBaseURL 0.000000e+00
API_FileIO_libcore.io.IoBridge_open 0.000000e+00
API_FileIO_android.content.ContextWrapper_openFileInput 0.000000e+00
API_FileIO_android.content.ContextWrapper_deleteFile 0.000000e+00
API_Database_android.content.ContextWrapper_deleteDatabase 0.000000e+00
API_Database_android.content.ContextWrapper_openOrCreateDatabase 0.000000e+00
API_Database_android.database.sqlite.SQLiteDatabase_openDatabase 0.000000e+00
API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict 0.000000e+00
API_WebView_android.webkit.WebView_loadUrl 0.000000e+00
API_JavaNativeInterface_java.lang.Runtime_load 0.000000e+00
API_DeviceData_android.content.ContentResolver_query -8.859358e-06
API_Database_android.database.sqlite.SQLiteDatabase_compileStatement -8.859358e-06
API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict -8.859358e-06
API_Binder_android.app.Activity_startActivity -8.859358e-06
API_Network_java.net.URL_openConnection -8.859358e-06
Memory_ProxyBinders -8.859358e-06
Memory_LocalBinders -8.859358e-06
API_Database_android.database.sqlite.SQLiteDatabase_insert -8.859358e-06
Memory_ParcelMemory -1.771872e-05
Memory_ViewRootImpl -2.657807e-05
Process_total -2.657807e-05
API_Database_android.database.sqlite.SQLiteDatabase_query -2.657807e-05
API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase -2.657807e-05
API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory -2.657807e-05
Memory_OpenSSLSockets -2.657807e-05
API_Database_android.database.sqlite.SQLiteDatabase_execSQL -3.543743e-05
Network_TotalReceivedPackets -3.543743e-05
Logcat_total -3.543743e-05
Memory_AppContexts -3.543743e-05
API_IPC_android.content.ContextWrapper_sendBroadcast -5.315615e-05
Memory_Activities -5.315615e-05
Memory_SharedClean -1.594684e-04
Network_TotalTransmittedPackets -6.467331e-04
Memory_HeapSize -1.240310e-03
Memory_PrivateClean -2.648948e-03
Network_TotalTransmittedBytes -4.332226e-03
Memory_HeapAlloc -5.714286e-03
Memory_PrivateDirty -6.121816e-03
Memory_PssClean -8.593577e-03
Memory_PssTotal -3.868882e-02
Out[85]:
| Feature | Importance | |
|---|---|---|
| 0 | API_Crypto-Hash_java.security.MessageDigest_up... | 0.000771 |
| 1 | API_Crypto-Hash_java.security.MessageDigest_di... | 0.000647 |
| 2 | Logcat_error | 0.000257 |
| 3 | API_Database_android.database.sqlite.SQLiteDat... | 0.000213 |
| 4 | Memory_SharedDirty | 0.000177 |
| ... | ... | ... |
| 137 | Network_TotalTransmittedBytes | -0.004332 |
| 138 | Memory_HeapAlloc | -0.005714 |
| 139 | Memory_PrivateDirty | -0.006122 |
| 140 | Memory_PssClean | -0.008594 |
| 141 | Memory_PssTotal | -0.038689 |
142 rows × 2 columns
SHAP
In [104]:
import shap
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
def explain_with_shap_pipeline(model, default_pipeline, model_name="Model"):
print(f"Generating SHAP explanations for {model_name}. This may take some time...")
X_sample = default_pipeline.dataset.X_train
if len(X_sample) > 100:
sample_indices = np.random.choice(len(X_sample), 100, replace=False)
X_sample_subset = X_sample.iloc[sample_indices]
else:
X_sample_subset = X_sample
try:
if hasattr(model, 'predict_proba'):
explainer = shap.Explainer(model, X_sample_subset)
shap_values = explainer(X_sample_subset, max_evals=100)
else:
explainer = shap.Explainer(model.predict, X_sample_subset)
shap_values = explainer(X_sample_subset, max_evals=100)
shap.summary_plot(shap_values, X_sample_subset, plot_type="bar", max_display=10)
plt.title(f"{model_name} - SHAP Feature Importance")
plt.tight_layout()
plt.show()
return shap_values
except Exception as e:
print(f"Error explaining {model_name} with SHAP: {str(e)}")
print("Skipping this model and continuing with others...")
return None
c:\Users\irina\ml-final-project\venv\Lib\site-packages\tqdm\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm
In [138]:
explain_with_shap_pipeline(gradientBoostingModel, default_pipeline, model_name="Gradient Boosting")
explain_with_shap_pipeline(logisticRegressionModel, default_pipeline, model_name="Logistic Regression")
explain_with_shap_pipeline(neuralNetworkModel, default_pipeline, model_name="Neural Network")
Generating SHAP explanations for Gradient Boosting. This may take some time... Error explaining Gradient Boosting with SHAP: The passed model is not callable and cannot be analyzed directly with the given masker! Model: GradientBoostingClassifier() Skipping this model and continuing with others... Generating SHAP explanations for Logistic Regression. This may take some time...
Generating SHAP explanations for Neural Network. This may take some time...
Error explaining Neural Network with SHAP: The passed model is not callable and cannot be analyzed directly with the given masker! Model: FeedForwardNeuralNetwork(activations=['relu', 'relu', 'relu', 'relu'],
batch_size=128, epochs=2,
kernel_initializer='glorot_uniform',
learning_rate=0.001, n_layers=4, num_classes=12,
num_features=142, units_per_layer=[512, 256, 128, 64])
Skipping this model and continuing with others...
LIME
In [89]:
import lime
import lime.lime_tabular
import matplotlib.pyplot as plt
import numpy as np
def explain_with_lime_pipeline(model, default_pipeline, model_name="Model", fixed_xlim=1.0):
X_train = default_pipeline.dataset.X_train
X_sample = X_train.iloc[0]
explainer = lime.lime_tabular.LimeTabularExplainer(
training_data=X_train.values,
feature_names=X_train.columns.tolist(),
mode="classification" if len(set(model.predict(X_train))) > 2 else "regression"
)
explanation = explainer.explain_instance(X_sample.values, model.predict_proba)
print(f"\nLIME Explanation for sample 0 using {model_name}:")
for feature, weight in explanation.as_list():
print(f"{feature}: {weight:.4f}")
fig = explanation.as_pyplot_figure()
fig.suptitle(f"LIME Explanation - {model_name}", fontsize=14)
# Forcing same x-axis range
ax = plt.gca()
ax.set_xlim(-fixed_xlim, fixed_xlim)
plt.tight_layout()
plt.show()
return explanation
In [90]:
explain_with_lime_pipeline(gradientBoostingModel, default_pipeline, model_name="Gradient Boosting")
c:\Users\irina\ml-final-project\venv\Lib\site-packages\sklearn\utils\validation.py:2739: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names warnings.warn(
LIME Explanation for sample 0 using Gradient Boosting: API_DexClassLoader_dalvik.system.DexFile_loadDex <= 0.00: -0.3408 API_DeviceData_android.accounts.AccountManager_getAccountsByType <= 0.00: -0.0839 API_DeviceData_android.location.Location_getLongitude <= 0.00: -0.0799 API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId <= 0.00: 0.0678 API_DexClassLoader_dalvik.system.DexFile_loadClass <= 0.00: -0.0655 API_IPC_android.content.ContextWrapper_startActivity <= 0.00: 0.0392 API_SystemManager_android.app.NotificationManager_notify <= 0.00: -0.0387 API_DeviceData_android.media.AudioRecord_startRecording <= 0.00: 0.0327 API_Binder_android.app.Activity_startActivity <= 0.00: -0.0308 API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled <= 0.00: 0.0297
C:\Users\irina\AppData\Local\Temp\ipykernel_20080\1343999492.py:29: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations. plt.tight_layout()
Out[90]:
<lime.explanation.Explanation at 0x18c4b53c320>
In [91]:
explain_with_lime_pipeline(logisticRegressionModel, default_pipeline, model_name="Logistic Regression")
c:\Users\irina\ml-final-project\venv\Lib\site-packages\sklearn\utils\validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names warnings.warn( C:\Users\irina\AppData\Local\Temp\ipykernel_20080\1343999492.py:29: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations. plt.tight_layout()
LIME Explanation for sample 0 using Logistic Regression: Network_TotalReceivedBytes > 11159.00: 0.0109 Memory_PssTotal <= 46037.00: 0.0090 API_DeviceData_android.location.Location_getLongitude <= 0.00: -0.0075 API_DeviceData_android.location.Location_getLatitude <= 0.00: 0.0045 Memory_PrivateDirty <= 31912.00: 0.0043 API_DeviceData_android.media.AudioRecord_startRecording <= 0.00: 0.0040 3643.00 < Network_TotalTransmittedBytes <= 16192.00: 0.0027 89684.00 < Memory_SharedClean <= 99176.00: -0.0024 API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled <= 0.00: -0.0020 API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase <= 0.00: -0.0019
Out[91]:
<lime.explanation.Explanation at 0x18c2f5d5370>
In [92]:
explain_with_lime_pipeline(neuralNetworkModel, default_pipeline, model_name="Neural Network")
1176/1176 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step 157/157 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step LIME Explanation for sample 0 using Neural Network: API_DeviceData_android.media.MediaRecorder_start <= 0.00: -0.4972 Network_TotalReceivedBytes > 11159.00: -0.1265 API_Database_android.content.ContextWrapper_databaseList > 0.00: 0.0752 API_DexClassLoader_dalvik.system.DexFile_loadClass <= 0.00: -0.0601 API_DeviceData_android.location.Location_getLatitude <= 0.00: 0.0595 API_DexClassLoader_dalvik.system.DexFile_loadDex <= 0.00: -0.0569 API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId <= 0.00: -0.0522 API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled <= 0.00: 0.0415 3643.00 < Network_TotalTransmittedBytes <= 16192.00: -0.0408 API_DeviceData_android.media.AudioRecord_startRecording <= 0.00: 0.0082
C:\Users\irina\AppData\Local\Temp\ipykernel_20080\1343999492.py:29: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all Axes decorations. plt.tight_layout()
Out[92]:
<lime.explanation.Explanation at 0x18c4b514800>
Model Calibration
In [ ]:
import matplotlib.pyplot as plt
from sklearn.calibration import calibration_curve
from sklearn.model_selection import train_test_split
from sklearn.calibration import CalibratedClassifierCV
def plot_multiclass_reliability_diagram(model, X, y, class_labels=None, model_name="Model"):
"""
Plot reliability diagrams for each class in a multiclass setting using one-vs-rest calibration curves.
"""
print(f"Generating multiclass reliability diagram for {model_name}...")
X_train, X_calib, y_train, y_calib = train_test_split(X, y, test_size=0.2, random_state=42)
calibrated_model = CalibratedClassifierCV(estimator=model, method='sigmoid', cv=3)
calibrated_model.fit(X_train, y_train)
y_probs = calibrated_model.predict_proba(X_calib)
n_classes = y_probs.shape[1]
if class_labels is None:
class_labels = list(range(n_classes))
plt.figure(figsize=(8, 6))
for i in range(n_classes):
y_true_bin = (y_calib == i).astype(int)
prob_true, prob_pred = calibration_curve(y_true_bin, y_probs[:, i], n_bins=10)
plt.plot(prob_pred, prob_true, marker='o', label=f"Class {class_labels[i]}")
plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfectly Calibrated')
plt.xlabel("Mean Predicted Probability")
plt.ylabel("True Fraction of Positives")
plt.title(f"Reliability Diagram - {model_name}")
plt.legend(loc="best")
plt.grid(True)
plt.tight_layout()
plt.show()
In [112]:
plot_multiclass_reliability_diagram(
logisticRegressionModel,
default_pipeline.dataset.X_train,
default_pipeline.dataset.y_train,
model_name="Logistic Regression"
)
Generating multiclass reliability diagram for Logistic Regression...
c:\Users\irina\ml-final-project\venv\Lib\site-packages\sklearn\linear_model\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\Users\irina\ml-final-project\venv\Lib\site-packages\sklearn\linear_model\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\Users\irina\ml-final-project\venv\Lib\site-packages\sklearn\linear_model\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
In [113]:
plot_multiclass_reliability_diagram(
gradientBoostingModel,
default_pipeline.dataset.X_train,
default_pipeline.dataset.y_train,
model_name="Gradient Boosting"
)
Generating multiclass reliability diagram for Gradient Boosting...
In [125]:
import matplotlib.pyplot as plt
from sklearn.calibration import CalibratedClassifierCV, calibration_curve
from sklearn.model_selection import train_test_split
import numpy as np
def plot_calibration_for_all_models(models_dict, X, y, model_name_prefix="Model", class_index=0, n_bins=10):
"""
Plot calibration curves (reliability diagrams) for multiple models on the same plot.
Parameters:
models_dict (dict): Dictionary with model names as keys and uncalibrated classifiers as values.
X (pd.DataFrame or np.ndarray): Feature data.
y (pd.Series or np.ndarray): Target labels.
model_name_prefix (str): Common prefix for model titles.
class_index (int): Which class to plot (for multiclass models).
n_bins (int): Number of bins to use in calibration curve.
"""
print("Generating calibration plots for all models...")
plt.figure(figsize=(8, 6))
X_train, X_calib, y_train, y_calib = train_test_split(X, y, test_size=0.2, random_state=42)
for name, model in models_dict.items():
try:
calibrated_model = CalibratedClassifierCV(estimator=model, method='sigmoid', cv=3)
calibrated_model.fit(X_train, y_train)
y_probs = calibrated_model.predict_proba(X_calib)
# Multiclass: select specific class
prob_true, prob_pred = calibration_curve((y_calib == class_index).astype(int), y_probs[:, class_index], n_bins=n_bins)
plt.plot(prob_pred, prob_true, marker='o', label=f"{model_name_prefix} {name}")
except Exception as e:
print(f"Skipped model '{name}' due to error: {e}")
plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Perfect Calibration')
plt.xlabel("Mean Predicted Probability")
plt.ylabel("True Fraction of Positives")
plt.title(f"Calibration Curve (Class {class_index})")
plt.legend(loc="best")
plt.grid(True)
plt.tight_layout()
plt.show()
In [128]:
all_models = {
"Gradient Boosting": gradientBoostingModel,
"Random Forest": randomForestModel,
"Decision Tree": decisionTreeModel,
"SVM - Non-linear": nonLinearSupportVectorModel,
"SVM - Linear": linearSupportVectorModel,
"Naive Bayes": naiveBayesModel,
"Neural Net": neuralNetworkModel,
"Baseline - Logistic Regression": logisticRegressionModel,
"Baseline - Majority Class": majorityClassModel,
}
In [131]:
plot_calibration_for_all_models(all_models, X_train, y_train, class_index=0)
Generating calibration plots for all models... Epoch 1/2 157/157 ━━━━━━━━━━━━━━━━━━━━ 4s 10ms/step - accuracy: 0.2280 - loss: 20327.3027 Epoch 2/2 15/157 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.2634 - loss: 3997.0261
c:\Users\irina\ml-final-project\venv\Lib\site-packages\keras\src\callbacks\early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
157/157 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.1962 - loss: 5617.5244 Skipped model 'Neural Net' due to error: FeedForwardNeuralNetwork should either be a classifier to be used with response_method=['decision_function', 'predict_proba'] or the response_method should be 'predict'. Got a regressor with response_method=['decision_function', 'predict_proba'] instead.
c:\Users\irina\ml-final-project\venv\Lib\site-packages\sklearn\linear_model\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\Users\irina\ml-final-project\venv\Lib\site-packages\sklearn\linear_model\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
c:\Users\irina\ml-final-project\venv\Lib\site-packages\sklearn\linear_model\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Skipped model 'Baseline - Majority Class' due to error: MajorityClassClassifier should either be a classifier to be used with response_method=['decision_function', 'predict_proba'] or the response_method should be 'predict'. Got a regressor with response_method=['decision_function', 'predict_proba'] instead.
Hyperparameter Optimization¶
- Bind the grids to each model in each pipeline
- Start all optimizations in parallel
In [74]:
pipeline_manager.pipeline_state = "in"
In [75]:
rf_grid = {
'n_estimators': [50, 100, 150, 200],
'max_depth': [None, 10, 20, 30],
'min_samples_split': [2, 5, 10],
'min_samples_leaf': [1, 2, 4,]
}
dt_grid = {
'criterion': ['gini', 'entropy'],
'max_depth': [None, 10, 20, 30],
'min_samples_split': [2, 5, 10],
'min_samples_leaf': [1, 2, 5],
'max_features': [None, 'sqrt', 'log2'],
'ccp_alpha': [0.0, 0.01, 0.1]
}
gnb_grid = {
'var_smoothing': Real(1e-12, 1e-6, prior='log-uniform')
}
In [76]:
modelNameToOptimizer = {
"Random Forest": {
"optimizer_type": "bayes",
"param_grid": rf_grid,
"max_iter": 1
},
"Decision Tree": {
"optimizer_type": "bayes",
"param_grid": dt_grid,
"max_iter": 1
},
"Naive Bayes": {
"optimizer_type": "bayes",
"param_grid": gnb_grid,
"max_iter": 1
},
"Feed Forward Neural Network": {
"optimizer_type": "bayes_nn",
"param_grid": None, # its hardcoded
"max_iter": 2,
"epochs": 2
}
}
In [77]:
optimized_models = pipeline_manager.all_pipelines_execute(methodName="modelling.fit_models",
exclude_pipeline_names=["stacking"],
current_phase=pipeline_manager.pipeline_state,
modelNameToOptimizer=modelNameToOptimizer)
Trial 2 Complete [00h 00m 02s]
val_accuracy: 0.4886242747306824
Best val_accuracy So Far: 0.4886242747306824
Total elapsed time: 00h 00m 03s
=> FINISHED OPTIMIZATION FOR Feed Forward Neural Network
Best params: {'n_layers': 4, 'units_0': 128, 'act_0': 'tanh', 'units_1': 128, 'act_1': 'tanh', 'units_2': 256, 'act_2': 'tanh', 'units_3': 64, 'act_3': 'tanh', 'learning_rate': 0.00012757332121604654}
=> Predicting Feed Forward Neural Network model
!> Started predicting Feed Forward Neural Network
Predicting training data
368/1176 ━━━━━━━━━━━━━━━━━━━━ 0s 273us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/saving/saving_lib.py:757: UserWarning: Skipping variable loading for optimizer 'adamw', because it has 2 variables whereas the saved optimizer has 22 variables. saveable.load_own_variables(weights_store.get(inner_path))
1176/1176 ━━━━━━━━━━━━━━━━━━━━ 0s 289us/step Predicting not training data 147/147 ━━━━━━━━━━━━━━━━━━━━ 0s 330us/step => Predicted Feed Forward Neural Network. Took 0.6260039806365967 seconds Optimized model Feed Forward Neural Network => Fitting Decision Tree model Model object: <library.phases.phases_implementation.modelling.shallow.model_definition.model_types.classifier.Classifier object at 0x292ee13d0> for Decision Tree Model object: <library.phases.phases_implementation.modelling.shallow.model_definition.model_types.classifier.Classifier object at 0x292ee13d0> => Fitting Naive Bayes model Model object: <library.phases.phases_implementation.modelling.shallow.model_definition.model_types.classifier.Classifier object at 0x294fe5050> for Naive Bayes Model object: <library.phases.phases_implementation.modelling.shallow.model_definition.model_types.classifier.Classifier object at 0x294fe5050> => STARTING OPTIMIZATION FOR Naive Bayes => STARTING OPTIMIZATION FOR Decision Tree Fitting 5 folds for each of 1 candidates, totalling 5 fits Fitting 5 folds for each of 1 candidates, totalling 5 fits [CV 1/5] END var_smoothing=2.888176653914472e-10;, score=0.180 total time= 0.1s [CV 2/5] END var_smoothing=2.888176653914472e-10;, score=0.188 total time= 0.1s [CV 3/5] END var_smoothing=2.888176653914472e-10;, score=0.197 total time= 0.1s [CV 4/5] END var_smoothing=2.888176653914472e-10;, score=0.188 total time= 0.1s [CV 5/5] END var_smoothing=2.888176653914472e-10;, score=0.175 total time= 0.1s => FINISHED OPTIMIZATION FOR Naive Bayes => Predicting Naive Bayes model !> Started predicting Naive Bayes Predicting training data Predicting not training data => Predicted Naive Bayes. Took 0.1727607250213623 seconds Optimized model Naive Bayes [CV 1/5] END ccp_alpha=0.01, criterion=entropy, max_depth=30, max_features=None, min_samples_leaf=5, min_samples_split=5;, score=0.608 total time= 1.3s [CV 2/5] END ccp_alpha=0.01, criterion=entropy, max_depth=30, max_features=None, min_samples_leaf=5, min_samples_split=5;, score=0.628 total time= 1.2s [CV 3/5] END ccp_alpha=0.01, criterion=entropy, max_depth=30, max_features=None, min_samples_leaf=5, min_samples_split=5;, score=0.624 total time= 1.3s [CV 4/5] END ccp_alpha=0.01, criterion=entropy, max_depth=30, max_features=None, min_samples_leaf=5, min_samples_split=5;, score=0.635 total time= 1.2s [CV 5/5] END ccp_alpha=0.01, criterion=entropy, max_depth=30, max_features=None, min_samples_leaf=5, min_samples_split=5;, score=0.618 total time= 1.2s => FINISHED OPTIMIZATION FOR Decision Tree => Predicting Decision Tree model !> Started predicting Decision Tree Predicting training data Predicting not training data => Predicted Decision Tree. Took 0.007951974868774414 seconds Optimized model Decision Tree
Lets visualize the resutls of the optimized models
In [78]:
pipeline_manager.pipelines["not_baseline"]["feed_forward_neural_network"].modelling.list_of_models
Out[78]:
{'Feed Forward Neural Network': <library.phases.phases_implementation.modelling.shallow.model_definition.model_types.classifier.Classifier at 0x282d72390>}
In [79]:
optimized_models
Out[79]:
{'not_baseline': {'ensembled': {},
'support_vector_machine': {},
'feed_forward_neural_network': {'Feed Forward Neural Network': FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])},
'naive_bayes': {'Naive Bayes': GaussianNB(var_smoothing=2.888176653914472e-10)},
'tree_based': {'Decision Tree': DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5)}},
'baseline': {'baselines': {}}}
If you want to see the cross validation progress for a given model you just need to:
Stacking Model: Training and Hyperparamter Optimization¶
In [80]:
estimators = []
for pipelineName, results in optimized_models["not_baseline"].items():
if isinstance(results, dict):
for modelName, modelObject in results.items():
estimators.append((modelName, modelObject))
estimators
Out[80]:
[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])),
('Naive Bayes', GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5))]
In [81]:
#Stacking model
stackingModel = StackingClassifier(
estimators=estimators,
final_estimator=LogisticRegression(),
cv=5,
verbose=3
)
stackingModel
Out[81]:
StackingClassifier(cv=5,
estimators=[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh',
'tanh',
'tanh',
'tanh'],
batch_size=128,
epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[128,
128,
256,
64])),
('Naive Bayes',
GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01,
criterion='entropy',
max_depth=30,
min_samples_leaf=5,
min_samples_split=5))],
final_estimator=LogisticRegression(), verbose=3)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
StackingClassifier(cv=5,
estimators=[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh',
'tanh',
'tanh',
'tanh'],
batch_size=128,
epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[128,
128,
256,
64])),
('Naive Bayes',
GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01,
criterion='entropy',
max_depth=30,
min_samples_leaf=5,
min_samples_split=5))],
final_estimator=LogisticRegression(), verbose=3)FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])GaussianNB(var_smoothing=2.888176653914472e-10)
DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5)LogisticRegression()
In [82]:
pipeline_manager.pipelines["not_baseline"]["stacking"].modelling.list_of_models["Stacking"].tuning_states["in"].assesment["model_sklearn"] = stackingModel
pipeline_manager.pipelines["not_baseline"]["stacking"].modelling.list_of_models["Stacking"].tuning_states["post"].assesment["model_sklearn"] = stackingModel
In [83]:
pipeline_manager.pipelines["not_baseline"]["stacking"].modelling.list_of_models["Stacking"].tuning_states["in"].model_sklearn = stackingModel
pipeline_manager.pipelines["not_baseline"]["stacking"].modelling.list_of_models["Stacking"].tuning_states["post"].model_sklearn = stackingModel
In [84]:
stackingModel.estimators
Out[84]:
[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])),
('Naive Bayes', GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5))]
In [85]:
pipeline_manager.pipelines["not_baseline"]["stacking"].modelling.list_of_models["Stacking"].tuning_states["in"].assesment["model_sklearn"]
Out[85]:
StackingClassifier(cv=5,
estimators=[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh',
'tanh',
'tanh',
'tanh'],
batch_size=128,
epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[128,
128,
256,
64])),
('Naive Bayes',
GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01,
criterion='entropy',
max_depth=30,
min_samples_leaf=5,
min_samples_split=5))],
final_estimator=LogisticRegression(), verbose=3)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
StackingClassifier(cv=5,
estimators=[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh',
'tanh',
'tanh',
'tanh'],
batch_size=128,
epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[128,
128,
256,
64])),
('Naive Bayes',
GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01,
criterion='entropy',
max_depth=30,
min_samples_leaf=5,
min_samples_split=5))],
final_estimator=LogisticRegression(), verbose=3)FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])GaussianNB(var_smoothing=2.888176653914472e-10)
DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5)LogisticRegression()
In [86]:
all_pipelines_to_exclude = []
for pipelineName, pipelineObject in pipeline_manager.pipelines["not_baseline"].items():
if pipelineName == "stacking":
continue
all_pipelines_to_exclude.append(pipelineName)
all_pipelines_to_exclude
Out[86]:
['ensembled', 'tree_based', 'support_vector_machine', 'naive_bayes', 'feed_forward_neural_network']
In [87]:
stacking_grid = {
'final_estimator__C': [0.01, 0.1, 1, 10],
'final_estimator__penalty': ['l2'],
'final_estimator__solver': ['lbfgs'],
'passthrough': [True, False]
}
In [88]:
modelNameToOptimizer = {
"Stacking": {
"optimizer_type": "bayes",
"param_grid": stacking_grid,
"max_iter": 1
}
}
In [89]:
optimized_models = pipeline_manager.all_pipelines_execute(methodName="modelling.fit_models",
current_phase=pipeline_manager.pipeline_state,
modelNameToOptimizer=modelNameToOptimizer
)
Gonna start fitting models in in phase Gonna start fitting models in in phase Gonna start fitting models in in phase Gonna start fitting models in in phase Gonna start fitting models in in phase Gonna start fitting models in in phase Gonna start fitting models in in phase Optimizing model Stacking
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/saving/saving_lib.py:757: UserWarning: Skipping variable loading for optimizer 'adamw', because it has 22 variables whereas the saved optimizer has 2 variables. saveable.load_own_variables(weights_store.get(inner_path))
=> Fitting Stacking model Model object: <library.phases.phases_implementation.modelling.shallow.model_definition.model_types.classifier.Classifier object at 0x2996e5110> for Stacking Model object: <library.phases.phases_implementation.modelling.shallow.model_definition.model_types.classifier.Classifier object at 0x2996e5110> => STARTING OPTIMIZATION FOR Stacking Fitting 5 folds for each of 1 candidates, totalling 5 fits 236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3108 - loss: 1.9936
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3213 - loss: 1.9604 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3163 - loss: 1.9748 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3084 - loss: 1.9640 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3178 - loss: 1.9663 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3116 - loss: 1.9658 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 416us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss
current = self.get_monitor_value(logs)
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 377us/step [CV 1/5] END final_estimator__C=0.1, final_estimator__penalty=l2, final_estimator__solver=lbfgs, passthrough=True;, score=0.184 total time= 14.1s 236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3370 - loss: 1.9423
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
146/189 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.2815 - loss: 2.0387
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.2971 - loss: 1.9930 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 393us/step 189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3020 - loss: 1.9906 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 388us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
147/189 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3164 - loss: 2.0063
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3287 - loss: 1.9641 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 394us/step 189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3222 - loss: 1.9661 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 407us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.3411 - loss: 1.9066 1/189 ━━━━━━━━━━━━━━━━━━━━ 4s 24ms/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 409us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 370us/step [CV 2/5] END final_estimator__C=0.1, final_estimator__penalty=l2, final_estimator__solver=lbfgs, passthrough=True;, score=0.178 total time= 14.6s 236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3318 - loss: 1.9514
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3308 - loss: 1.9394 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3217 - loss: 1.9858 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3403 - loss: 1.9240 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 400us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
145/189 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.2792 - loss: 2.0772
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.2990 - loss: 2.0196 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step 189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3065 - loss: 1.9773 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 400us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss
current = self.get_monitor_value(logs)
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 379us/step [CV 3/5] END final_estimator__C=0.1, final_estimator__penalty=l2, final_estimator__solver=lbfgs, passthrough=True;, score=0.227 total time= 14.5s 236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3414 - loss: 1.9273
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3104 - loss: 2.0080 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 447us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.3207 - loss: 1.9561 1/189 ━━━━━━━━━━━━━━━━━━━━ 5s 31ms/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 515us/step 189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3484 - loss: 1.8953 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3180 - loss: 1.9444 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 401us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
185/189 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3287 - loss: 1.9478
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3300 - loss: 1.9440 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 401us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 367us/step [CV 4/5] END final_estimator__C=0.1, final_estimator__penalty=l2, final_estimator__solver=lbfgs, passthrough=True;, score=0.187 total time= 14.3s 236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3284 - loss: 1.9868
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3166 - loss: 1.9825 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 436us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3552 - loss: 1.9200 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 490us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3209 - loss: 1.9375 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 429us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3219 - loss: 1.9723 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 410us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
189/189 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3238 - loss: 1.9291 189/189 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss
current = self.get_monitor_value(logs)
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 374us/step [CV 5/5] END final_estimator__C=0.1, final_estimator__penalty=l2, final_estimator__solver=lbfgs, passthrough=True;, score=0.196 total time= 14.4s 294/294 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3511 - loss: 1.8872
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3469 - loss: 1.8870 1/236 ━━━━━━━━━━━━━━━━━━━━ 7s 31ms/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step 236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.3230 - loss: 1.9409
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 377us/step 236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3544 - loss: 1.9021 178/236 ━━━━━━━━━━━━━━━━━━━━ 0s 284us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 384us/step 236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3269 - loss: 1.9253 175/236 ━━━━━━━━━━━━━━━━━━━━ 0s 287us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step 236/236 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.3112 - loss: 1.9586 170/236 ━━━━━━━━━━━━━━━━━━━━ 0s 297us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/keras/src/callbacks/early_stopping.py:153: UserWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: accuracy,loss current = self.get_monitor_value(logs)
236/236 ━━━━━━━━━━━━━━━━━━━━ 0s 393us/step
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
=> FINISHED OPTIMIZATION FOR Stacking
=> Predicting Stacking model
ESTIMTORS AT PREDICTION ARE: [('Feed Forward Neural Network', FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])), ('Naive Bayes', GaussianNB(var_smoothing=2.888176653914472e-10)), ('Decision Tree', DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5))]
!> Started predicting Stacking
Predicting training data
1176/1176 ━━━━━━━━━━━━━━━━━━━━ 0s 365us/step
Predicting not training data
147/147 ━━━━━━━━━━━━━━━━━━━━ 0s 358us/step
=> Predicted Stacking. Took 0.9399738311767578 seconds
Optimized model Stacking
In [90]:
optimized_models
Out[90]:
{'not_baseline': {'naive_bayes': {},
'tree_based': {},
'feed_forward_neural_network': {},
'ensembled': {},
'support_vector_machine': {},
'stacking': {'Stacking': StackingClassifier(cv=5,
estimators=[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh',
'tanh',
'tanh',
'tanh'],
batch_size=128,
epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[128,
128,
256,
64])),
('Naive Bayes',
GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01,
criterion='entropy',
max_depth=30,
min_samples_leaf=5,
min_samples_split=5))],
final_estimator=LogisticRegression(C=0.1), passthrough=True,
verbose=3)}},
'baseline': {'baselines': {}}}
In-tuning evaluation¶
In [91]:
pipeline_manager.pipelines["not_baseline"]["stacking"].modelling.list_of_models["Stacking"].tuning_states["in"].assesment["model_sklearn"]
Out[91]:
StackingClassifier(cv=5,
estimators=[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh',
'tanh',
'tanh',
'tanh'],
batch_size=128,
epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[128,
128,
256,
64])),
('Naive Bayes',
GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01,
criterion='entropy',
max_depth=30,
min_samples_leaf=5,
min_samples_split=5))],
final_estimator=LogisticRegression(C=0.1), passthrough=True,
verbose=3)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
StackingClassifier(cv=5,
estimators=[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh',
'tanh',
'tanh',
'tanh'],
batch_size=128,
epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[128,
128,
256,
64])),
('Naive Bayes',
GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01,
criterion='entropy',
max_depth=30,
min_samples_leaf=5,
min_samples_split=5))],
final_estimator=LogisticRegression(C=0.1), passthrough=True,
verbose=3)FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])GaussianNB(var_smoothing=2.888176653914472e-10)
DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5)LogisticRegression(C=0.1)
In [92]:
model_results = pipeline_manager.all_pipelines_execute(methodName="modelling.evaluate_and_store_models",
exclude_category="baseline",
current_phase=pipeline_manager.pipeline_state)
Evaluating model Feed Forward Neural Network
Evaluating Feed Forward Neural Network in in phase
METRIC RESULTS FOR Feed Forward Neural Network => F1: 0.4528579852668521, Precision: 0.4609710584377584, Recall: 0.4886242823729534, Accuracy: 0.4886242823729534, Kappa: 0.3443050844274128 (val), 0.32828693479457693 (train)
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Feed Forward Neural Network', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 3.294156074523926, 'timeToPredict': 0.6260039806365967, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': array([7, 7, 5, ..., 7, 7, 0]), 'predictions_train': array([ 0, 0, 0, ..., 11, 5, 5]), 'predictions_test': None, 'model_sklearn': FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64]), 'metrics': {'base_metrics': {'accuracy': 0.4886242823729534, 'precision': 0.4609710584377584, 'recall': 0.4886242823729534, 'f1-score': 0.4528579852668521}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.3443050844274128)}, 'train': {'kappa_train': np.float64(0.32828693479457693)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
Evaluating model Decision Tree Evaluating Decision Tree in in phase Evaluating model Naive Bayes Evaluating Naive Bayes in in phase METRIC RESULTS FOR Decision Tree => F1: 0.6125515235728184, Precision: 0.6008532417688947, Recall: 0.6372528173506272, Accuracy: 0.6372528173506272, Kappa: 0.5429647385324186 (val), 0.5369492899857313 (train) METRIC RESULTS FOR Naive Bayes => F1: 0.18472547385256674, Precision: 0.4681097658581059, Recall: 0.1917924728896449, Accuracy: 0.1917924728896449, Kappa: 0.12100693788825734 (val), 0.12148659785849225 (train) Evaluating model Stacking Evaluating Stacking in in phase METRIC RESULTS FOR Stacking => F1: 0.2158752985054583, Precision: 0.26034620679953024, Recall: 0.3308526472464384, Accuracy: 0.3308526472464384, Kappa: 0.057863662135795524 (val), 0.046379579077391764 (train)
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Naive Bayes', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 0.4284400939941406, 'timeToPredict': 0.1727607250213623, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': array([4, 4, 6, ..., 4, 6, 6]), 'predictions_train': array([4, 6, 4, ..., 1, 4, 4]), 'predictions_test': None, 'model_sklearn': GaussianNB(var_smoothing=2.888176653914472e-10), 'metrics': {'base_metrics': {'accuracy': 0.1917924728896449, 'precision': 0.4681097658581059, 'recall': 0.1917924728896449, 'f1-score': 0.18472547385256674}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.12100693788825734)}, 'train': {'kappa_train': np.float64(0.12148659785849225)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
****WARNING****: A model with the same values already exists in the results. Results will not be saved.
You tried to write {'id': 'e2250069c6cae278a344d419d5b5696f2eae4cf26e21f9faab5456524d89e420', 'timeStamp': '2025-05-10 17:36:15', 'comments': 'UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME', 'modelName': 'Naive Bayes', 'currentPhase': 'in', 'features_used': ['Memory_PssTotal', 'Memory_PssClean', 'Memory_SharedDirty', 'Memory_PrivateDirty', 'Memory_SharedClean', 'Memory_PrivateClean', 'Memory_SwapPssDirty', 'Memory_HeapSize', 'Memory_HeapAlloc', 'Memory_HeapFree', 'Memory_Views', 'Memory_ViewRootImpl', 'Memory_AppContexts', 'Memory_Activities', 'Memory_Assets', 'Memory_AssetManagers', 'Memory_LocalBinders', 'Memory_ProxyBinders', 'Memory_ParcelMemory', 'Memory_ParcelCount', 'Memory_DeathRecipients', 'Memory_OpenSSLSockets', 'Memory_WebViews', 'API_Process_android.os.Process_start', 'API_Process_android.app.ActivityManager_killBackgroundProcesses', 'API_Process_android.os.Process_killProcess', 'API_Command_java.lang.Runtime_exec', 'API_Command_java.lang.ProcessBuilder_start', 'API_JavaNativeInterface_java.lang.Runtime_loadLibrary', 'API_JavaNativeInterface_java.lang.Runtime_load', 'API_WebView_android.webkit.WebView_loadUrl', 'API_WebView_android.webkit.WebView_loadData', 'API_WebView_android.webkit.WebView_loadDataWithBaseURL', 'API_WebView_android.webkit.WebView_addJavascriptInterface', 'API_WebView_android.webkit.WebView_evaluateJavascript', 'API_WebView_android.webkit.WebView_postUrl', 'API_WebView_android.webkit.WebView_postWebMessage', 'API_WebView_android.webkit.WebView_savePassword', 'API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled', 'API_FileIO_libcore.io.IoBridge_open', 'API_FileIO_android.content.ContextWrapper_openFileInput', 'API_FileIO_android.content.ContextWrapper_openFileOutput', 'API_FileIO_android.content.ContextWrapper_deleteFile', 'API_Database_android.content.ContextWrapper_openOrCreateDatabase', 'API_Database_android.content.ContextWrapper_databaseList', 'API_Database_android.content.ContextWrapper_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_execSQL', 'API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_getPath', 'API_Database_android.database.sqlite.SQLiteDatabase_insert', 'API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow', 'API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_openDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_query', 'API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQuery', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_update', 'API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_compileStatement', 'API_Database_android.database.sqlite.SQLiteDatabase_create', 'API_IPC_android.content.ContextWrapper_sendBroadcast', 'API_IPC_android.content.ContextWrapper_sendStickyBroadcast', 'API_IPC_android.content.ContextWrapper_startActivity', 'API_IPC_android.content.ContextWrapper_startService', 'API_IPC_android.content.ContextWrapper_stopService', 'API_IPC_android.content.ContextWrapper_registerReceiver', 'API_Binder_android.app.ContextImpl_registerReceiver', 'API_Binder_android.app.ActivityThread_handleReceiver', 'API_Binder_android.app.Activity_startActivity', 'API_Crypto_javax.crypto.spec.SecretKeySpec_$init', 'API_Crypto_javax.crypto.Cipher_doFinal', 'API_Crypto-Hash_java.security.MessageDigest_digest', 'API_Crypto-Hash_java.security.MessageDigest_update', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId', 'API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName', 'API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID', 'API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion', 'API_DeviceInfo_android.os.Debug_isDebuggerConnected', 'API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages', 'API_Network_java.net.URL_openConnection', 'API_Network_org.apache.http.impl.client.AbstractHttpClient_execute', 'API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream', 'API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary', 'API_DexClassLoader_dalvik.system.DexFile_loadDex', 'API_DexClassLoader_dalvik.system.DexFile_loadClass', 'API_DexClassLoader_dalvik.system.DexClassLoader_$init', 'API_Base64_android.util.Base64_decode', 'API_Base64_android.util.Base64_encode', 'API_Base64_android.util.Base64_encodeToString', 'API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting', 'API_SystemManager_android.app.NotificationManager_notify', 'API_SystemManager_android.telephony.TelephonyManager_listen', 'API_SystemManager_android.content.BroadcastReceiver_abortBroadcast', 'API_SMS_android.telephony.SmsManager_sendTextMessage', 'API_SMS_android.telephony.SmsManager_sendMultipartTextMessage', 'API_DeviceData_android.content.ContentResolver_query', 'API_DeviceData_android.content.ContentResolver_registerContentObserver', 'API_DeviceData_android.content.ContentResolver_insert', 'API_DeviceData_android.content.ContentResolver_delete', 'API_DeviceData_android.accounts.AccountManager_getAccountsByType', 'API_DeviceData_android.accounts.AccountManager_getAccounts', 'API_DeviceData_android.location.Location_getLatitude', 'API_DeviceData_android.location.Location_getLongitude', 'API_DeviceData_android.media.AudioRecord_startRecording', 'API_DeviceData_android.media.MediaRecorder_start', 'API_DeviceData_android.os.SystemProperties_get', 'API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages', 'API__sessions', 'Network_TotalReceivedBytes', 'Network_TotalReceivedPackets', 'Network_TotalTransmittedBytes', 'Network_TotalTransmittedPackets', 'Battery_wakelock', 'Battery_service', 'Logcat_info', 'Logcat_error', 'Logcat_warning', 'Logcat_debug', 'Logcat_verbose', 'Logcat_total', 'Process_total', 'Reboot_before'], 'hyperParameters': "{'priors': None, 'var_smoothing': 2.888176653914472e-10}", 'timeToFit': 0.4284400939941406, 'timeToPredict': 0.1727607250213623, 'accuracy_val': 0.1917924728896449, 'accuracy_test': -1, 'precision_val': 0.4681097658581059, 'precision_test': -1, 'recall_val': 0.1917924728896449, 'recall_test': -1, 'f1-score_val': 0.18472547385256674, 'f1-score_test': -1, 'kappa_val': np.float64(0.12100693788825734), 'kappa_test': -1}
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Decision Tree', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 7.729022979736328, 'timeToPredict': 0.007951974868774414, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': array([7, 7, 5, ..., 0, 7, 7]), 'predictions_train': array([0, 0, 7, ..., 0, 7, 5]), 'predictions_test': None, 'model_sklearn': DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5), 'metrics': {'base_metrics': {'accuracy': 0.6372528173506272, 'precision': 0.6008532417688947, 'recall': 0.6372528173506272, 'f1-score': 0.6125515235728184}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.5429647385324186)}, 'train': {'kappa_train': np.float64(0.5369492899857313)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Stacking', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 89.45169520378113, 'timeToPredict': 0.9399738311767578, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': array([5, 5, 5, ..., 5, 5, 5]), 'predictions_train': array([5, 0, 5, ..., 7, 5, 5]), 'predictions_test': None, 'model_sklearn': StackingClassifier(cv=5,
estimators=[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh',
'tanh',
'tanh',
'tanh'],
batch_size=128,
epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[128,
128,
256,
64])),
('Naive Bayes',
GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01,
criterion='entropy',
max_depth=30,
min_samples_leaf=5,
min_samples_split=5))],
final_estimator=LogisticRegression(C=0.1), passthrough=True,
verbose=3), 'metrics': {'base_metrics': {'accuracy': 0.3308526472464384, 'precision': 0.26034620679953024, 'recall': 0.3308526472464384, 'f1-score': 0.2158752985054583}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.057863662135795524)}, 'train': {'kappa_train': np.float64(0.046379579077391764)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
Cross-Model Evaluation (in-tuning)¶
In [93]:
pipeline_manager.pipelines["baseline"]["baselines"].modelling.list_of_models["Majority Class (baseline)"].tuning_states["in"].assesment
Out[93]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Majority Class (baseline)',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': None,
'timeToPredict': None,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': None,
'predictions_train': None,
'predictions_test': None,
'model_sklearn': None}
In [94]:
import math
In [95]:
pipeline_manager.pipelines_analysis.plot_cross_model_comparison(save_plots=False, save_path="")
PLOTTING CROSS MODEL COMPARISON FOR in PHASE Plotting: accuracy Plotting: precision Plotting: recall Plotting: f1-score Plotting: kappa
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:197: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() /Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:197: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend()
In [96]:
pipeline_manager.pipelines_analysis.plot_results_df(metrics=["timeToFit", "timeToPredict"], save_plots=False, save_path="")
# Note I still have not added a way to extract the fitting time from the in-tuning phase
Metrics df: id timeStamp \
0 243a6598001fa06499ad0568b821fd168ca055810b52aa... 2025-05-10 17:36:15
comments modelName currentPhase \
0 UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME Decision Tree in
features_used \
0 [Memory_PssTotal, Memory_PssClean, Memory_Shar...
hyperParameters timeToFit \
0 {'ccp_alpha': 0.01, 'class_weight': None, 'cri... 7.729023
timeToPredict accuracy_val accuracy_test precision_val precision_test \
0 0.007952 0.637253 -1 0.600853 -1
recall_val recall_test f1-score_val f1-score_test kappa_val kappa_test
0 0.637253 -1 0.612552 -1 0.542965 -1
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:291: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=metrics_df, x='modelName', y=metric, ax=ax, palette="viridis") /Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:291: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=metrics_df, x='modelName', y=metric, ax=ax, palette="viridis")
Out[96]:
| id | timeStamp | comments | modelName | currentPhase | features_used | hyperParameters | timeToFit | timeToPredict | accuracy_val | accuracy_test | precision_val | precision_test | recall_val | recall_test | f1-score_val | f1-score_test | kappa_val | kappa_test | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 243a6598001fa06499ad0568b821fd168ca055810b52aa... | 2025-05-10 17:36:15 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Decision Tree | in | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {'ccp_alpha': 0.01, 'class_weight': None, 'cri... | 7.729023 | 0.007952 | 0.637253 | -1 | 0.600853 | -1 | 0.637253 | -1 | 0.612552 | -1 | 0.542965 | -1 |
| 0 | e2250069c6cae278a344d419d5b5696f2eae4cf26e21f9... | 2025-05-10 17:36:15 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Naive Bayes | in | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {'priors': None, 'var_smoothing': 2.8881766539... | 0.428440 | 0.172761 | 0.191792 | -1 | 0.468110 | -1 | 0.191792 | -1 | 0.184725 | -1 | 0.121007 | -1 |
| 0 | 37bccda89ba05aeb33922c08f53eea01e34816826f5905... | 2025-05-10 17:36:08 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Feed Forward Neural Network | in | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {'num_features': 142, 'num_classes': 12, 'batc... | 3.294156 | 0.626004 | 0.488624 | -1 | 0.460971 | -1 | 0.488624 | -1 | 0.452858 | -1 | 0.344305 | -1 |
| 0 | 0780855ecb8235dc797bab5a61b018312434c27684041b... | 2025-05-10 17:36:16 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Stacking | in | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {'cv': 5, 'estimators': [('Feed Forward Neural... | 89.451695 | 0.939974 | 0.330853 | -1 | 0.260346 | -1 | 0.330853 | -1 | 0.215875 | -1 | 0.057864 | -1 |
Results Summary Visualization¶
In [97]:
pipeline_manager.pipelines_analysis.plot_results_summary(training_metric="timeToPredict", performance_metric="kappa", save_plots=False, save_path="")
Intra-model Evaluation (in-tuning)¶
We plot 3 metrics (f1, recall and precision) for each model constrasting its performance between trainign and validation. It is not subject of interest cross-model evaluation, but rather, intra-model evaluation.
** EVALUATE HERE THE NOT OPTIMIZED VERSION TOO **
In [98]:
pipeline_manager.pipelines_analysis.plot_intra_model_comparison(save_plots=False, save_path="")
METRICS IS None
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() /Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() /Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() /Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend()
Unfortunately, keras does not store the per-epoch data when doing Bayesian hyperparemter optimization
In [99]:
#pipeline_manager.pipelines_analysis.plot_per_epoch_progress(metrics=["accuracy", "loss"])
Residual analysis (in-tuning)¶
This contains the confusion matrices (weighted and not weighted). It also returns the specific elements that were erroneously classified. The analysis of this residuals may be meaningful for further inspection (clustering analysis, EDA...)
In [100]:
residuals, confusion_matrices = pipeline_manager.pipelines_analysis.plot_confusion_matrix(save_plots=False, save_path="")
residuals
Plotting: Decision Tree Plotting: Naive Bayes Plotting: Feed Forward Neural Network Plotting: Stacking
Out[100]:
{'tree_based': 53099 3
11712 0
52498 3
41946 10
2662 0
..
1005 10
36413 5
17232 9
33099 7
36519 5
Length: 1706, dtype: int64,
'naive_bayes': 53099 3
30660 7
24323 5
11712 0
52498 3
..
44394 7
7212 0
7612 0
46313 7
36519 5
Length: 3801, dtype: int64,
'feed_forward_neural_network': 53099 3
52498 3
46186 7
41946 10
38536 5
..
17232 9
52857 3
47610 7
7612 0
36519 5
Length: 2405, dtype: int64,
'stacking': 53099 3
30660 7
11712 0
52498 3
42114 11
..
47610 7
44394 7
7212 0
7612 0
46313 7
Length: 3147, dtype: int64}
Optimizer Covergance¶
Before we proceed, note that you can access the optimizser object (grid, random or bayes) as per shown in the following example. You may see more detailed information on the tuning process there. For now, we limit ourselves to solely plot the convergence map of the bayes optimizer
Questions:
- what can further be analyzed from this process in order to get better insights?
In [101]:
pipeline_manager.pipelines["not_baseline"]["ensembled"].modelling.list_of_models["Random Forest"].tuning_states["in"].assesment # just an example
Out[101]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Random Forest',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': None,
'timeToPredict': None,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': None,
'predictions_train': None,
'predictions_test': None,
'model_sklearn': None}
In [102]:
#pipeline_manager.all_pipelines_execute(methodName="modelling.plot_convergence")
In [103]:
pipeline_manager.pipelines["baseline"]["baselines"].modelling.list_of_models["Majority Class (baseline)"].tuning_states["post"].assesment # just an example
Out[103]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Majority Class (baseline)',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': None,
'timeToPredict': None,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': None,
'predictions_train': None,
'predictions_test': None,
'model_sklearn': None}
In [104]:
pipeline_manager.pipelines["not_baseline"]["feed_forward_neural_network"].modelling.list_of_models["Feed Forward Neural Network"].tuning_states["post"].assesment # just an example
Out[104]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Feed Forward Neural Network',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': None,
'timeToPredict': None,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': None,
'predictions_train': None,
'predictions_test': None,
'model_sklearn': FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])}
In [105]:
pipeline_manager.pipelines["not_baseline"]["feed_forward_neural_network"].modelling.list_of_models["Feed Forward Neural Network"].tuning_states["post"].assesment # just an example
Out[105]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Feed Forward Neural Network',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': None,
'timeToPredict': None,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': None,
'predictions_train': None,
'predictions_test': None,
'model_sklearn': FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])}
Post-tuning Evaluation¶
Lets start off by selecting the best perfoming model
In [106]:
best_model, best_score = pipeline_manager.select_best_performing_model(metric="f1-score")
Best performing model: Decision Tree with f1-score 0.6126
In [107]:
pipeline_manager.fit_final_models()
Gonna start fitting models in post phase
=> Fitting Decision Tree model
Sklearn model: DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5)
!> Started fitting Decision Tree
X_train_combined: (42328, 142)
Lenght of X_data: 42328
=> Fitted Decision Tree. Took 1.6952228546142578 seconds
=> Predicting Decision Tree model
!> Started predicting Decision Tree
=> Predicted Decision Tree. Took 0.009786844253540039 seconds
Fitted and predicted model Decision Tree
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/utils/validation.py:2732: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names warnings.warn(
Gonna start fitting models in post phase => Fitting Majority Class (baseline) model Sklearn model: MajorityClassClassifier() !> Started fitting Majority Class (baseline) X_train_combined: (42328, 142) Lenght of X_data: 42328 => Fitted Majority Class (baseline). Took 0.0123748779296875 seconds => Predicting Majority Class (baseline) model !> Started predicting Majority Class (baseline) => Predicted Majority Class (baseline). Took 7.486343383789062e-05 seconds Fitted and predicted model Majority Class (baseline)
In [108]:
pipeline_manager.pipelines["not_baseline"]["feed_forward_neural_network"].modelling.list_of_models["Feed Forward Neural Network"].tuning_states["post"].assesment
Out[108]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Feed Forward Neural Network',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': None,
'timeToPredict': None,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': None,
'predictions_train': None,
'predictions_test': None,
'model_sklearn': FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])}
In [109]:
pipeline_manager.evaluate_store_final_models()
Evaluating model Decision Tree Evaluating Decision Tree in post phase METRIC RESULTS FOR Decision Tree => F1: 0.6131239537118284, Precision: 0.6238891387196049, Recall: 0.6343537414965986, Accuracy: 0.6343537414965986, Kappa: 0.535538758060095 (val), 0.5390467012500681 (train)
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Decision Tree', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 1.6952228546142578, 'timeToPredict': 0.009786844253540039, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': None, 'predictions_train': array([0, 0, 5, ..., 0, 7, 7]), 'predictions_test': array([ 5, 9, 0, ..., 5, 11, 0]), 'model_sklearn': DecisionTreeClassifier(ccp_alpha=0.01, criterion='entropy', max_depth=30,
min_samples_leaf=5, min_samples_split=5), 'metrics': {'base_metrics': {'accuracy': 0.6343537414965986, 'precision': 0.6238891387196049, 'recall': 0.6343537414965986, 'f1-score': 0.6131239537118284}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.535538758060095)}, 'train': {'kappa_train': np.float64(0.5390467012500681)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
Evaluating model Majority Class (baseline)
Evaluating Majority Class (baseline) in post phase
METRIC RESULTS FOR Majority Class (baseline) => F1: 0.13946974395051273, Precision: 0.09074131619059188, Recall: 0.3012329931972789, Accuracy: 0.3012329931972789, Kappa: 0.0 (val), 0.0 (train)
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
METADATA IS: {'id': None, 'timeStamp': None, 'comments': None, 'modelName': 'Majority Class (baseline)', 'currentPhase': None, 'features_used': None, 'hyperParameters': None, 'timeToFit': 0.0123748779296875, 'timeToPredict': 7.486343383789062e-05, 'accuracy': None, 'precision': None, 'recall': None, 'f1-score': None, 'kappa': None, 'predictions_val': None, 'predictions_train': [np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), 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np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5), np.int64(5)], 'model_sklearn': MajorityClassClassifier(), 'metrics': {'base_metrics': {'accuracy': 0.3012329931972789, 'precision': 0.09074131619059188, 'recall': 0.3012329931972789, 'f1-score': 0.13946974395051273}, 'additional_metrics': {'not_train': {'kappa': np.float64(0.0)}, 'train': {'kappa_train': np.float64(0.0)}}}}
COL IS: accuracy
COL IS: precision
COL IS: recall
COL IS: f1-score
COL IS: kappa
****WARNING****: A model with the same values already exists in the results. Results will not be saved.
You tried to write {'id': 'e8049b3117f069a3bf6fcc7cd8b302f34aaf5264c0da28067da15d8481aa4bee', 'timeStamp': '2025-05-10 17:36:39', 'comments': 'UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME', 'modelName': 'Majority Class (baseline)', 'currentPhase': 'post', 'features_used': ['Memory_PssTotal', 'Memory_PssClean', 'Memory_SharedDirty', 'Memory_PrivateDirty', 'Memory_SharedClean', 'Memory_PrivateClean', 'Memory_SwapPssDirty', 'Memory_HeapSize', 'Memory_HeapAlloc', 'Memory_HeapFree', 'Memory_Views', 'Memory_ViewRootImpl', 'Memory_AppContexts', 'Memory_Activities', 'Memory_Assets', 'Memory_AssetManagers', 'Memory_LocalBinders', 'Memory_ProxyBinders', 'Memory_ParcelMemory', 'Memory_ParcelCount', 'Memory_DeathRecipients', 'Memory_OpenSSLSockets', 'Memory_WebViews', 'API_Process_android.os.Process_start', 'API_Process_android.app.ActivityManager_killBackgroundProcesses', 'API_Process_android.os.Process_killProcess', 'API_Command_java.lang.Runtime_exec', 'API_Command_java.lang.ProcessBuilder_start', 'API_JavaNativeInterface_java.lang.Runtime_loadLibrary', 'API_JavaNativeInterface_java.lang.Runtime_load', 'API_WebView_android.webkit.WebView_loadUrl', 'API_WebView_android.webkit.WebView_loadData', 'API_WebView_android.webkit.WebView_loadDataWithBaseURL', 'API_WebView_android.webkit.WebView_addJavascriptInterface', 'API_WebView_android.webkit.WebView_evaluateJavascript', 'API_WebView_android.webkit.WebView_postUrl', 'API_WebView_android.webkit.WebView_postWebMessage', 'API_WebView_android.webkit.WebView_savePassword', 'API_WebView_android.webkit.WebView_setHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_getHttpAuthUsernamePassword', 'API_WebView_android.webkit.WebView_setWebContentsDebuggingEnabled', 'API_FileIO_libcore.io.IoBridge_open', 'API_FileIO_android.content.ContextWrapper_openFileInput', 'API_FileIO_android.content.ContextWrapper_openFileOutput', 'API_FileIO_android.content.ContextWrapper_deleteFile', 'API_Database_android.content.ContextWrapper_openOrCreateDatabase', 'API_Database_android.content.ContextWrapper_databaseList', 'API_Database_android.content.ContextWrapper_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_execSQL', 'API_Database_android.database.sqlite.SQLiteDatabase_deleteDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_getPath', 'API_Database_android.database.sqlite.SQLiteDatabase_insert', 'API_Database_android.database.sqlite.SQLiteDatabase_insertOrThrow', 'API_Database_android.database.sqlite.SQLiteDatabase_insertWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_openDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_openOrCreateDatabase', 'API_Database_android.database.sqlite.SQLiteDatabase_query', 'API_Database_android.database.sqlite.SQLiteDatabase_queryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQuery', 'API_Database_android.database.sqlite.SQLiteDatabase_rawQueryWithFactory', 'API_Database_android.database.sqlite.SQLiteDatabase_update', 'API_Database_android.database.sqlite.SQLiteDatabase_updateWithOnConflict', 'API_Database_android.database.sqlite.SQLiteDatabase_compileStatement', 'API_Database_android.database.sqlite.SQLiteDatabase_create', 'API_IPC_android.content.ContextWrapper_sendBroadcast', 'API_IPC_android.content.ContextWrapper_sendStickyBroadcast', 'API_IPC_android.content.ContextWrapper_startActivity', 'API_IPC_android.content.ContextWrapper_startService', 'API_IPC_android.content.ContextWrapper_stopService', 'API_IPC_android.content.ContextWrapper_registerReceiver', 'API_Binder_android.app.ContextImpl_registerReceiver', 'API_Binder_android.app.ActivityThread_handleReceiver', 'API_Binder_android.app.Activity_startActivity', 'API_Crypto_javax.crypto.spec.SecretKeySpec_$init', 'API_Crypto_javax.crypto.Cipher_doFinal', 'API_Crypto-Hash_java.security.MessageDigest_digest', 'API_Crypto-Hash_java.security.MessageDigest_update', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSubscriberId', 'API_DeviceInfo_android.telephony.TelephonyManager_getLine1Number', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperator', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkOperatorName', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimOperatorName', 'API_DeviceInfo_android.net.wifi.WifiInfo_getMacAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getBSSID', 'API_DeviceInfo_android.net.wifi.WifiInfo_getIpAddress', 'API_DeviceInfo_android.net.wifi.WifiInfo_getNetworkId', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getSimSerialNumber', 'API_DeviceInfo_android.telephony.TelephonyManager_getNetworkCountryIso', 'API_DeviceInfo_android.telephony.TelephonyManager_getDeviceSoftwareVersion', 'API_DeviceInfo_android.os.Debug_isDebuggerConnected', 'API_DeviceInfo_android.content.pm.PackageManager_getInstallerPackageName', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledApplications', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledModules', 'API_DeviceInfo_android.content.pm.PackageManager_getInstalledPackages', 'API_Network_java.net.URL_openConnection', 'API_Network_org.apache.http.impl.client.AbstractHttpClient_execute', 'API_Network_com.android.okhttp.internal.huc.HttpURLConnectionImpl_getInputStream', 'API_Network_com.android.okhttp.internal.http.HttpURLConnectionImpl_getInputStream', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResource', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findResources', 'API_DexClassLoader_dalvik.system.BaseDexClassLoader_findLibrary', 'API_DexClassLoader_dalvik.system.DexFile_loadDex', 'API_DexClassLoader_dalvik.system.DexFile_loadClass', 'API_DexClassLoader_dalvik.system.DexClassLoader_$init', 'API_Base64_android.util.Base64_decode', 'API_Base64_android.util.Base64_encode', 'API_Base64_android.util.Base64_encodeToString', 'API_SystemManager_android.app.ApplicationPackageManager_setComponentEnabledSetting', 'API_SystemManager_android.app.NotificationManager_notify', 'API_SystemManager_android.telephony.TelephonyManager_listen', 'API_SystemManager_android.content.BroadcastReceiver_abortBroadcast', 'API_SMS_android.telephony.SmsManager_sendTextMessage', 'API_SMS_android.telephony.SmsManager_sendMultipartTextMessage', 'API_DeviceData_android.content.ContentResolver_query', 'API_DeviceData_android.content.ContentResolver_registerContentObserver', 'API_DeviceData_android.content.ContentResolver_insert', 'API_DeviceData_android.content.ContentResolver_delete', 'API_DeviceData_android.accounts.AccountManager_getAccountsByType', 'API_DeviceData_android.accounts.AccountManager_getAccounts', 'API_DeviceData_android.location.Location_getLatitude', 'API_DeviceData_android.location.Location_getLongitude', 'API_DeviceData_android.media.AudioRecord_startRecording', 'API_DeviceData_android.media.MediaRecorder_start', 'API_DeviceData_android.os.SystemProperties_get', 'API_DeviceData_android.app.ApplicationPackageManager_getInstalledPackages', 'API__sessions', 'Network_TotalReceivedBytes', 'Network_TotalReceivedPackets', 'Network_TotalTransmittedBytes', 'Network_TotalTransmittedPackets', 'Battery_wakelock', 'Battery_service', 'Logcat_info', 'Logcat_error', 'Logcat_warning', 'Logcat_debug', 'Logcat_verbose', 'Logcat_total', 'Process_total', 'Reboot_before'], 'hyperParameters': '{}', 'timeToFit': 0.0123748779296875, 'timeToPredict': 7.486343383789062e-05, 'accuracy_test': 0.3012329931972789, 'accuracy_val': -1, 'precision_test': 0.09074131619059188, 'precision_val': -1, 'recall_test': 0.3012329931972789, 'recall_val': -1, 'f1-score_test': 0.13946974395051273, 'f1-score_val': -1, 'kappa_test': np.float64(0.0), 'kappa_val': -1}
Metric Evaluation¶
In [110]:
pipeline_manager.pipelines["not_baseline"]["feed_forward_neural_network"].modelling.list_of_models["Feed Forward Neural Network"].tuning_states["post"].assesment
Out[110]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Feed Forward Neural Network',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': None,
'timeToPredict': None,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': None,
'predictions_train': None,
'predictions_test': None,
'model_sklearn': FeedForwardNeuralNetwork(activations=['tanh', 'tanh', 'tanh', 'tanh'],
batch_size=128, epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654, n_layers=4,
num_classes=12, num_features=142,
units_per_layer=[128, 128, 256, 64])}
In [111]:
pipeline_manager.pipeline_state = "post"
Cross-Model Evaluation (post-tuning)¶
In [112]:
class_report_df = pipeline_manager.pipelines_analysis._compute_classification_report(include_training=True)
class_report_df
Out[112]:
| precision | recall | f1-score | support | accuracy | kappa | precision | recall | f1-score | support | ... | f1-score | support | accuracy | kappa | precision | recall | f1-score | support | accuracy | kappa | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Adware | 0.528381 | 0.698148 | 0.601516 | 1080.0 | 0.634354 | 0.535539 | 0.538614 | 0.712222 | 0.61337 | 9900.0 | ... | 0.0 | 1080.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 9900.0 | 0.298526 | 0.0 |
| Backdoor | 0.0 | 0.0 | 0.0 | 113.0 | 0.634354 | 0.535539 | 0.0 | 0.0 | 0.0 | 1024.0 | ... | 0.0 | 113.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 1024.0 | 0.298526 | 0.0 |
| FileInfector | 0.0 | 0.0 | 0.0 | 20.0 | 0.634354 | 0.535539 | 0.0 | 0.0 | 0.0 | 228.0 | ... | 0.0 | 20.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 228.0 | 0.298526 | 0.0 |
| PUA | 0.707865 | 0.5625 | 0.626866 | 112.0 | 0.634354 | 0.535539 | 0.664865 | 0.417657 | 0.513034 | 1178.0 | ... | 0.0 | 112.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 1178.0 | 0.298526 | 0.0 |
| Ransomware | 0.640091 | 0.769863 | 0.699005 | 365.0 | 0.634354 | 0.535539 | 0.637595 | 0.772817 | 0.698724 | 3046.0 | ... | 0.0 | 365.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 3046.0 | 0.298526 | 0.0 |
| Riskware | 0.668308 | 0.766408 | 0.714004 | 1417.0 | 0.634354 | 0.535539 | 0.664959 | 0.749842 | 0.704854 | 12636.0 | ... | 0.462996 | 1417.0 | 0.301233 | 0.0 | 0.298526 | 1.0 | 0.459792 | 12636.0 | 0.298526 | 0.0 |
| Scareware | 0.86 | 0.467391 | 0.605634 | 92.0 | 0.634354 | 0.535539 | 0.841438 | 0.501259 | 0.628256 | 794.0 | ... | 0.0 | 92.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 794.0 | 0.298526 | 0.0 |
| Trojan | 0.699176 | 0.609581 | 0.651312 | 835.0 | 0.634354 | 0.535539 | 0.69627 | 0.618785 | 0.655244 | 7602.0 | ... | 0.0 | 835.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 7602.0 | 0.298526 | 0.0 |
| Trojan_Banker | 0.0 | 0.0 | 0.0 | 30.0 | 0.634354 | 0.535539 | 0.0 | 0.0 | 0.0 | 211.0 | ... | 0.0 | 30.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 211.0 | 0.298526 | 0.0 |
| Trojan_Dropper | 0.696203 | 0.337423 | 0.454545 | 163.0 | 0.634354 | 0.535539 | 0.788732 | 0.39801 | 0.529051 | 1407.0 | ... | 0.0 | 163.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 1407.0 | 0.298526 | 0.0 |
| Trojan_SMS | 0.619048 | 0.122066 | 0.203922 | 213.0 | 0.634354 | 0.535539 | 0.590551 | 0.130359 | 0.213574 | 1726.0 | ... | 0.0 | 213.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 1726.0 | 0.298526 | 0.0 |
| Trojan_Spy | 0.742222 | 0.632576 | 0.683027 | 264.0 | 0.634354 | 0.535539 | 0.76297 | 0.662267 | 0.709061 | 2576.0 | ... | 0.0 | 264.0 | 0.301233 | 0.0 | 0.0 | 0.0 | 0.0 | 2576.0 | 0.298526 | 0.0 |
| macro avg | 0.513441 | 0.41383 | 0.436652 | 4704.0 | 0.634354 | 0.535539 | 0.515499 | 0.413602 | 0.438764 | 42328.0 | ... | 0.038583 | 4704.0 | 0.301233 | 0.0 | 0.024877 | 0.083333 | 0.038316 | 42328.0 | 0.298526 | 0.0 |
| weighted avg | 0.623889 | 0.634354 | 0.613124 | 4704.0 | 0.634354 | 0.535539 | 0.626432 | 0.637049 | 0.617348 | 42328.0 | ... | 0.13947 | 4704.0 | 0.301233 | 0.0 | 0.089118 | 0.298526 | 0.13726 | 42328.0 | 0.298526 | 0.0 |
| modelName | Decision Tree | Decision Tree | Decision Tree | Decision Tree | Decision Tree | Decision Tree | Decision Tree_train | Decision Tree_train | Decision Tree_train | Decision Tree_train | ... | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline) | Majority Class (baseline)_train | Majority Class (baseline)_train | Majority Class (baseline)_train | Majority Class (baseline)_train | Majority Class (baseline)_train | Majority Class (baseline)_train |
15 rows × 24 columns
In [113]:
pipeline_manager.pipelines_analysis.plot_cross_model_comparison(save_plots=False, save_path="")
PLOTTING CROSS MODEL COMPARISON FOR post PHASE Plotting: accuracy Plotting: precision Plotting: recall Plotting: f1-score Plotting: kappa
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:197: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() /Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:197: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend()
In [114]:
pipeline_manager.pipelines_analysis.plot_results_df(metrics=["timeToFit", "timeToPredict"], save_plots=False, save_path="")
Metrics df: id timeStamp \
0 6fb5192ce0863bf0c7e6ea8ea204629a92df20dd83b605... 2025-05-10 17:36:35
comments modelName currentPhase \
0 UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME Decision Tree post
features_used \
0 [Memory_PssTotal, Memory_PssClean, Memory_Shar...
hyperParameters timeToFit \
0 {'ccp_alpha': 0.01, 'class_weight': None, 'cri... 1.695223
timeToPredict accuracy_test accuracy_val precision_test precision_val \
0 0.009787 0.634354 -1 0.623889 -1
recall_test recall_val f1-score_test f1-score_val kappa_test kappa_val
0 0.634354 -1 0.613124 -1 0.539047 -1
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:291: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=metrics_df, x='modelName', y=metric, ax=ax, palette="viridis") /Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:291: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.barplot(data=metrics_df, x='modelName', y=metric, ax=ax, palette="viridis")
Out[114]:
| id | timeStamp | comments | modelName | currentPhase | features_used | hyperParameters | timeToFit | timeToPredict | accuracy_test | accuracy_val | precision_test | precision_val | recall_test | recall_val | f1-score_test | f1-score_val | kappa_test | kappa_val | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6fb5192ce0863bf0c7e6ea8ea204629a92df20dd83b605... | 2025-05-10 17:36:35 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Decision Tree | post | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {'ccp_alpha': 0.01, 'class_weight': None, 'cri... | 1.695223 | 0.009787 | 0.634354 | -1 | 0.623889 | -1 | 0.634354 | -1 | 0.613124 | -1 | 0.539047 | -1 |
| 0 | e8049b3117f069a3bf6fcc7cd8b302f34aaf5264c0da28... | 2025-05-10 17:36:39 | UPDATED KAPPA NAME IN THE CSV, ONE SECOND TIME | Majority Class (baseline) | post | [Memory_PssTotal, Memory_PssClean, Memory_Shar... | {} | 0.012375 | 0.000075 | 0.301233 | -1 | 0.090741 | -1 | 0.301233 | -1 | 0.139470 | -1 | 0.000000 | -1 |
Results Summary Visualization¶
In [115]:
pipeline_manager.pipelines_analysis.plot_results_summary(training_metric="timeToFit", performance_metric="accuracy", save_plots=False, save_path="")
Intra-model Evaluation (post-tuning)¶
We plot 3 metrics (f1, recall and precision) for each model constrasting its performance between trainign and validation. It is not subject of interest cross-model evaluation, but rather, intra-model evaluation.
In [116]:
pipeline_manager.pipelines_analysis.plot_intra_model_comparison(save_plots=False, save_path="")
METRICS IS None
/Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend() /Users/javierdominguezsegura/Academics/College/Courses/2nd year/ML/finalProject/ml-final-project/library/pipeline/analysis/pipelines_analysis.py:258: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. ax.legend()
Lets see the progress of the NN
In [117]:
#pipeline_manager.pipelines_analysis.plot_per_epoch_progress(metrics=["accuracy", "loss"])
Residual analysis (post-tuning)¶
This contains the confusion matrices (weighted and not weighted). It also returns the specific elements that were erroneously classified. The analysis of this residuals may be meaningful for further inspection (clustering analysis, EDA...)
In [118]:
residuals, confusion_matrices = pipeline_manager.pipelines_analysis.plot_confusion_matrix(save_plots=False, save_path="")
residuals
Plotting: Decision Tree Plotting: Majority Class (baseline)
Out[118]:
{'tree_based': 155 10
40893 5
5338 0
34312 2
19104 5
..
32942 7
15442 1
10913 0
32109 7
36199 5
Length: 1720, dtype: int64,
'baselines': 15929 9
8975 0
155 10
51708 11
1482 6
..
10913 0
52384 3
32109 7
42424 11
5520 0
Length: 3287, dtype: int64}
In [119]:
pipeline_manager.pipelines["not_baseline"]["stacking"].modelling.list_of_models["Stacking"].tuning_states["post"].assesment
Out[119]:
{'id': None,
'timeStamp': None,
'comments': None,
'modelName': 'Stacking',
'currentPhase': None,
'features_used': None,
'hyperParameters': None,
'timeToFit': None,
'timeToPredict': None,
'accuracy': None,
'precision': None,
'recall': None,
'f1-score': None,
'kappa': None,
'predictions_val': None,
'predictions_train': None,
'predictions_test': None,
'model_sklearn': StackingClassifier(cv=5,
estimators=[('Feed Forward Neural Network',
FeedForwardNeuralNetwork(activations=['tanh',
'tanh',
'tanh',
'tanh'],
batch_size=128,
epochs=1,
kernel_initializer='glorot_uniform',
learning_rate=0.00012757332121604654,
n_layers=4,
num_classes=12,
num_features=142,
units_per_layer=[128,
128,
256,
64])),
('Naive Bayes',
GaussianNB(var_smoothing=2.888176653914472e-10)),
('Decision Tree',
DecisionTreeClassifier(ccp_alpha=0.01,
criterion='entropy',
max_depth=30,
min_samples_leaf=5,
min_samples_split=5))],
final_estimator=LogisticRegression(C=0.1), passthrough=True,
verbose=3)}
Saving Best Model¶
In [120]:
#pipeline_manager.serialize_models(models_to_serialize=["Majority Class (baseline)"])
In [121]:
#pipeline_manager.serialize_pipelines(pipelines_to_serialize=["naive-bayes"])